Template:Article of the week - Revision history

Shawndouglas: Updated article of the week text

2024-06-17T15:05:39Z

Updated article of the week text

← Older revision		Revision as of 15:05, 17 June 2024
Line 1:		Line 1:
	<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:~~Fig2 Berezin PLoSCompBio23 19~~-12.~~png~~\|240px]]</div>		<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Soto-Perdomo SoftwareX2023 24.jpg\|240px]]</div>
	'''"[[Journal:~~Ten simple rules~~ for ~~managing laboratory information~~\|~~Ten simple rules~~ for ~~managing laboratory information~~]]"'''		'''"[[Journal:OptiGUI DataCollector: A graphical user interface for automating the data collecting process in optical and photonics labs\|OptiGUI DataCollector: A graphical user interface for automating the data collecting process in optical and photonics labs]]"'''

	~~[[Information]]~~ is ~~the cornerstone of [[research]], from experimental~~ data~~/[[metadata]]~~ and ~~computational processes to complex inventories of reagents~~ and equipment. ~~These 10 simple rules discuss best practices~~ for ~~leveraging~~ [[~~laboratory information management system~~]]s ~~(LIMS) to transform this large information load into useful scientific findings~~. ~~The development of [[mathematical model]]s~~ that ~~can predict the properties of biological systems is the holy grail~~ of [[~~computational biology~~]]. ~~Such models can be used to test biological hypotheses, guide the development of biomanufactured products, engineer new systems meeting user-defined specifications, and much more~~ ... ('''[[Journal:~~Ten simple rules~~ for ~~managing laboratory information~~\|Full article...]]''')<br />		OptiGUI DataCollector is a Python 3.8-based graphical user interface (GUI) that facilitates automated data collection in optics and photonics research and development equipment. It provides an intuitive and easy-to-use platform for controlling a wide range of optical instruments, including [[spectrometer]]s and lasers. OptiGUI DataCollector is a flexible and modular framework that enables simple integration with different types of devices. It simplifies experimental workflow and reduces human error by automating parameter control, data acquisition, and [[Data analysis\|analysis]]. OptiGUI DataCollector is currently focused on optical mode conversion utilizing fiber optic technologies ... ('''[[Journal:OptiGUI DataCollector: A graphical user interface for automating the data collecting process in optical and photonics labs\|Full article...]]''')<br />

	''Recently featured'':		''Recently featured'':
	{{flowlist \|		{{flowlist \|
			* [[Journal:Ten simple rules for managing laboratory information\|Ten simple rules for managing laboratory information]]
	* [[Journal:Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology\|Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology]]		* [[Journal:Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology\|Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology]]
	* [[Journal:Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study\|Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study]]		* [[Journal:Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study\|Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study]]
	* [[Journal:Judgements of research co-created by generative AI: Experimental evidence\|Judgements of research co-created by generative AI: Experimental evidence]]
	}}		}}

Shawndouglas: Updated article of the week text

2024-06-10T18:03:14Z

Updated article of the week text

← Older revision		Revision as of 18:03, 10 June 2024
Line 1:		Line 1:
	<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:~~Fig1 Signoroni NatComm23 14~~.png\|240px]]</div>		<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Berezin PLoSCompBio23 19-12.png\|240px]]</div>
	'''"[[Journal:~~Hierarchical AI enables global interpretation~~ of ~~culture plates in~~ the ~~era~~ of ~~digital microbiology\|Hierarchical AI enables global interpretation~~ of ~~culture plates in~~ the ~~era~~ of ~~digital microbiology~~]]"'''		'''"[[Journal:Ten simple rules for managing laboratory information\|Ten simple rules for managing laboratory information]]"'''

			[[Information]] is the cornerstone of [[research]], from experimental data/[[metadata]] and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging [[laboratory information management system]]s (LIMS) to transform this large information load into useful scientific findings. The development of [[mathematical model]]s that can predict the properties of biological systems is the holy grail of [[computational biology]]. Such models can be used to test biological hypotheses, guide the development of biomanufactured products, engineer new systems meeting user-defined specifications, and much more ... ('''[[Journal:Ten simple rules for managing laboratory information\|Full article...]]''')<br />

	Full [[laboratory automation]] is revolutionizing work habits in an increasing number of clinical [[microbiology]] facilities worldwide, generating huge streams of [[Imaging\|digital images]] for interpretation. Contextually, [[deep learning]] (DL) architectures are leading to paradigm shifts in the way computers can assist with difficult visual interpretation tasks in several domains. At the crossroads of these epochal trends, we present a system able to tackle a core task in clinical microbiology, namely the global interpretation of diagnostic [[Bacteria\|bacterial]] [[Cell culture\|culture]] plates, including presumptive [[pathogen]] identification. This is achieved by decomposing the problem into a hierarchy of complex subtasks and addressing them with a multi-network architecture we call DeepColony ... ('''[[Journal:Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology\|Full article...]]''')<br />
	''Recently featured'':		''Recently featured'':
	{{flowlist \|		{{flowlist \|
			* [[Journal:Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology\|Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology]]
	* [[Journal:Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study\|Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study]]		* [[Journal:Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study\|Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study]]
	* [[Journal:Judgements of research co-created by generative AI: Experimental evidence\|Judgements of research co-created by generative AI: Experimental evidence]]		* [[Journal:Judgements of research co-created by generative AI: Experimental evidence\|Judgements of research co-created by generative AI: Experimental evidence]]
	* [[Journal:Geochemical biodegraded oil classification using a machine learning approach\|Geochemical biodegraded oil classification using a machine learning approach]]
	}}		}}

Shawndouglas: Updated article of the week text.

2024-06-03T15:02:27Z

Updated article of the week text.

← Older revision		Revision as of 15:02, 3 June 2024
Line 1:		Line 1:
	<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 ~~Čartolovni DigitalHealth2023 9~~.~~jpeg~~\|240px]]</div>		<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Signoroni NatComm23 14.png\|240px]]</div>
	'''"[[Journal:~~Critical analysis~~ of the ~~impact~~ of AI ~~on the patient–physician relationship: A multi-stakeholder qualitative study\|Critical analysis~~ of the ~~impact~~ of ~~AI on the patient–physician relationship: A multi-stakeholder qualitative study~~]]"'''		'''"[[Journal:Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology\|Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology]]"'''

	~~This qualitative study aims to present the aspirations, expectations~~, ~~and critical analysis~~ of ~~the potential~~ for [[~~artificial intelligence~~]] (AI) to ~~transform~~ the ~~patient–physician relationship, according to multi-stakeholder insight~~. ~~This study was conducted from June to December 2021, using an anticipatory ethics approach and sociology of expectations as~~ the ~~theoretical frameworks. It focused mainly on three groups~~ of ~~stakeholders~~, namely ~~physicians (''n'' = 12), patients (''n'' = 15), and healthcare managers (''n'' = 11), all of whom are directly related to~~ the ~~adoption~~ of ~~AI in medicine (''n'' = 38)~~. ~~In this study, interviews were conducted with 40% of the patients in~~ the ~~sample (15/38), as well as 31%~~ of ~~the physicians (12/38)~~ and ~~29% of health managers in the sample (11/38)~~ ... ('''[[Journal:~~Critical analysis~~ of the ~~impact~~ of ~~AI on the patient–physician relationship: A multi-stakeholder qualitative study~~\|Full article...]]''')<br />		Full [[laboratory automation]] is revolutionizing work habits in an increasing number of clinical [[microbiology]] facilities worldwide, generating huge streams of [[Imaging\|digital images]] for interpretation. Contextually, [[deep learning]] (DL) architectures are leading to paradigm shifts in the way computers can assist with difficult visual interpretation tasks in several domains. At the crossroads of these epochal trends, we present a system able to tackle a core task in clinical microbiology, namely the global interpretation of diagnostic [[Bacteria\|bacterial]] [[Cell culture\|culture]] plates, including presumptive [[pathogen]] identification. This is achieved by decomposing the problem into a hierarchy of complex subtasks and addressing them with a multi-network architecture we call DeepColony ... ('''[[Journal:Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology\|Full article...]]''')<br />
	''Recently featured'':		''Recently featured'':
	{{flowlist \|		{{flowlist \|
			* [[Journal:Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study\|Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study]]
	* [[Journal:Judgements of research co-created by generative AI: Experimental evidence\|Judgements of research co-created by generative AI: Experimental evidence]]		* [[Journal:Judgements of research co-created by generative AI: Experimental evidence\|Judgements of research co-created by generative AI: Experimental evidence]]
	* [[Journal:Geochemical biodegraded oil classification using a machine learning approach\|Geochemical biodegraded oil classification using a machine learning approach]]		* [[Journal:Geochemical biodegraded oil classification using a machine learning approach\|Geochemical biodegraded oil classification using a machine learning approach]]
	* [[Journal:Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study\|Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study]]
	}}		}}

Shawndouglas: Updated article of the week text

2024-05-26T15:48:13Z

Updated article of the week text

← Older revision		Revision as of 15:48, 26 May 2024
Line 1:		Line 1:
	<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 ~~Niszczota EconBusRev23~~ 9-2.~~png~~\|240px]]</div>		<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Čartolovni DigitalHealth2023 9.jpeg\|240px]]</div>
	'''"[[Journal:~~Judgements~~ of ~~research co-created by generative~~ AI: ~~Experimental evidence~~\|~~Judgements~~ of ~~research co-created by generative~~ AI: ~~Experimental evidence~~]]"'''		'''"[[Journal:Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study\|Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study]]"'''

	~~The introduction~~ of ~~[[ChatGPT]] has fuelled a public debate on~~ the ~~appropriateness of using generative~~ [[artificial intelligence]] (AI) (~~[[large language model]]s or LLMs~~) ~~in work~~, ~~including a debate on how they might be used~~ (~~and abused~~) ~~by researchers. In the current work~~, ~~we test whether delegating parts of the research process to LLMs leads people to distrust researchers~~ and ~~devalues their scientific work. Participants~~ (''N'' = ~~402~~) ~~considered a researcher who delegates elements~~ of the ~~research process to a PhD student or LLM and rated three aspects~~ of ~~such delegation~~. ~~Firstly~~, ~~they rated whether it is morally appropriate to do so. Secondly~~, ~~they judged whether—after deciding to delegate~~ the ~~research process—they would trust~~ the ~~scientist~~ (~~who decided to delegate~~) ~~to oversee future projects~~ ... ('''[[Journal:~~Judgements~~ of ~~research co-created by generative~~ AI: ~~Experimental evidence~~\|Full article...]]''')<br />		This qualitative study aims to present the aspirations, expectations, and critical analysis of the potential for [[artificial intelligence]] (AI) to transform the patient–physician relationship, according to multi-stakeholder insight. This study was conducted from June to December 2021, using an anticipatory ethics approach and sociology of expectations as the theoretical frameworks. It focused mainly on three groups of stakeholders, namely physicians (''n'' = 12), patients (''n'' = 15), and healthcare managers (''n'' = 11), all of whom are directly related to the adoption of AI in medicine (''n'' = 38). In this study, interviews were conducted with 40% of the patients in the sample (15/38), as well as 31% of the physicians (12/38) and 29% of health managers in the sample (11/38) ... ('''[[Journal:Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study\|Full article...]]''')<br />
	''Recently featured'':		''Recently featured'':
	{{flowlist \|		{{flowlist \|
			* [[Journal:Judgements of research co-created by generative AI: Experimental evidence\|Judgements of research co-created by generative AI: Experimental evidence]]
	* [[Journal:Geochemical biodegraded oil classification using a machine learning approach\|Geochemical biodegraded oil classification using a machine learning approach]]		* [[Journal:Geochemical biodegraded oil classification using a machine learning approach\|Geochemical biodegraded oil classification using a machine learning approach]]
	* [[Journal:Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study\|Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study]]		* [[Journal:Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study\|Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study]]
	* [[Journal:Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study\|Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study]]
	}}		}}

Shawndouglas: Updated article of the week text

2024-05-20T15:26:13Z

Updated article of the week text

← Older revision		Revision as of 15:26, 20 May 2024
Line 1:		Line 1:
	<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 ~~Bispo~~-~~Silva Geosciences23 13-11~~.png\|240px]]</div>		<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Niszczota EconBusRev23 9-2.png\|240px]]</div>
	'''"[[Journal:~~Geochemical biodegraded oil classification using a machine learning approach~~\|~~Geochemical biodegraded oil classification using a machine learning approach~~]]"'''		'''"[[Journal:Judgements of research co-created by generative AI: Experimental evidence\|Judgements of research co-created by generative AI: Experimental evidence]]"'''

	[[~~Chromatography\|Chromatographic~~]] ~~oil analysis is an important step for~~ the identification of biodegraded petroleum via peak visualization and interpretation of phenomena that explain the oil geochemistry. However, analyses of chromatogram components by geochemists are comparative, visual, and consequently slow. This article aims to improve the chromatogram analysis process performed during geochemical interpretation by proposing the use of [[~~convolutional neural network~~]]s ~~(CNN~~), ~~which are deep learning techniques widely~~ used by ~~big tech companies~~. ~~Two hundred~~ and ~~twenty-one~~ (~~221~~) ~~chromatographic oil images from different worldwide basins (Brazil, USA, Portugal, Angola,~~ and ~~Venezuela) were used~~. ~~The [[open-source software]] Orange Data Mining was used~~ to ~~process images by CNN~~. ~~The CNN algorithm extracts~~, ~~pixel by pixel, recurring features from~~ the ~~images through convolutional operations~~ ... ('''[[Journal:~~Geochemical biodegraded oil classification using a machine learning approach~~\|Full article...]]''')<br />		The introduction of [[ChatGPT]] has fuelled a public debate on the appropriateness of using generative [[artificial intelligence]] (AI) ([[large language model]]s or LLMs) in work, including a debate on how they might be used (and abused) by researchers. In the current work, we test whether delegating parts of the research process to LLMs leads people to distrust researchers and devalues their scientific work. Participants (''N'' = 402) considered a researcher who delegates elements of the research process to a PhD student or LLM and rated three aspects of such delegation. Firstly, they rated whether it is morally appropriate to do so. Secondly, they judged whether—after deciding to delegate the research process—they would trust the scientist (who decided to delegate) to oversee future projects ... ('''[[Journal:Judgements of research co-created by generative AI: Experimental evidence\|Full article...]]''')<br />
	''Recently featured'':		''Recently featured'':
	{{flowlist \|		{{flowlist \|
			* [[Journal:Geochemical biodegraded oil classification using a machine learning approach\|Geochemical biodegraded oil classification using a machine learning approach]]
	* [[Journal:Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study\|Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study]]		* [[Journal:Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study\|Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study]]
	* [[Journal:Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study\|Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study]]		* [[Journal:Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study\|Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study]]
	* [[Journal:Why do we need food systems informatics? Introduction to this special collection on smart and connected regional food systems\|Why do we need food systems informatics? Introduction to this special collection on smart and connected regional food systems]]
	}}		}}

Shawndouglas: Updated article of the week text

2024-05-13T13:37:09Z

Updated article of the week text

← Older revision		Revision as of 13:37, 13 May 2024
Line 1:		Line 1:
	<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 ~~Mishra JofNepMedAss23 61~~-~~258~~.png\|~~220px~~]]</div>		<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Bispo-Silva Geosciences23 13-11.png\|240px]]</div>
	'''"[[Journal:~~Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of~~ a ~~tertiary care center: A descriptive cross-sectional study~~\|~~Knowledge of internal quality control for laboratory tests among laboratory personnel working in~~ a ~~biochemistry department of a tertiary care center: A descriptive cross-sectional study~~]]"'''		'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach\|Geochemical biodegraded oil classification using a machine learning approach]]"'''

	~~The~~ [[~~clinical laboratory~~]] ~~holds a central position in patient care,~~ and ~~as such, ensuring accurate [[laboratory]] test results is a necessity. Internal [[quality control]] (QC) ensures day-to-day laboratory consistency~~. However, ~~unless practiced~~, the ~~success~~ of ~~laboratory~~ [[~~quality management system~~]]s (~~QMSs~~) ~~cannot be achieved~~. ~~This depends on the efforts~~ and ~~commitment of laboratory personnel for its implementation~~. ~~Hence, the aim of this study~~ was to ~~find out the knowledge of internal QC for laboratory tests among laboratory personnel working in the Department of Biochemistry~~, ~~B.P. Koirala Institute of Health Sciences (BPKIHS)~~, ~~a tertiary care center~~ ... ('''[[Journal:~~Knowledge of internal quality control for laboratory tests among laboratory personnel working in~~ a ~~biochemistry department of a tertiary care center: A descriptive cross-sectional study~~\|Full article...]]''')<br />		[[Chromatography\|Chromatographic]] oil analysis is an important step for the identification of biodegraded petroleum via peak visualization and interpretation of phenomena that explain the oil geochemistry. However, analyses of chromatogram components by geochemists are comparative, visual, and consequently slow. This article aims to improve the chromatogram analysis process performed during geochemical interpretation by proposing the use of [[convolutional neural network]]s (CNN), which are deep learning techniques widely used by big tech companies. Two hundred and twenty-one (221) chromatographic oil images from different worldwide basins (Brazil, USA, Portugal, Angola, and Venezuela) were used. The [[open-source software]] Orange Data Mining was used to process images by CNN. The CNN algorithm extracts, pixel by pixel, recurring features from the images through convolutional operations ... ('''[[Journal:Geochemical biodegraded oil classification using a machine learning approach\|Full article...]]''')<br />
	''Recently featured'':		''Recently featured'':
	{{flowlist \|		{{flowlist \|
			* [[Journal:Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study\|Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study]]
	* [[Journal:Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study\|Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study]]		* [[Journal:Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study\|Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study]]
	* [[Journal:Why do we need food systems informatics? Introduction to this special collection on smart and connected regional food systems\|Why do we need food systems informatics? Introduction to this special collection on smart and connected regional food systems]]		* [[Journal:Why do we need food systems informatics? Introduction to this special collection on smart and connected regional food systems\|Why do we need food systems informatics? Introduction to this special collection on smart and connected regional food systems]]
	* [[Journal:Data management challenges for artificial intelligence in plant and agricultural research\|Data management challenges for artificial intelligence in plant and agricultural research]]
	}}		}}

Shawndouglas: Updated article of the week text

2024-05-06T18:14:21Z

Updated article of the week text

← Older revision		Revision as of 18:14, 6 May 2024
Line 1:		Line 1:
	<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 ~~Karaattuthazhathu NatJLabMed23 12~~-2.png\|~~260px~~]]</div>		<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Mishra JofNepMedAss23 61-258.png\|220px]]</div>
	'''"[[Journal:~~Sigma metrics as a valuable tool for effective analytical performance and~~ quality control ~~planning~~ in ~~the clinical laboratory~~: A ~~retrospective~~ study\|~~Sigma metrics as a valuable tool for effective analytical performance and~~ quality control ~~planning~~ in ~~the clinical laboratory~~: A ~~retrospective~~ study]]"'''		'''"[[Journal:Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study\|Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study]]"'''

	~~For the release of precise~~ and accurate ~~reports of~~ [[~~Medical test\|routine tests~~]]~~, its necessary to follow~~ a ~~proper~~ [[quality ~~management system~~]] (~~QMS~~) in the [[~~clinical laboratory~~]]. ~~As one of~~ the ~~most popular QMS tools for process improvement, Six Sigma techniques~~ and ~~tools have been accepted widely in the [[~~laboratory~~]] testing process~~. ~~Six Sigma gives an objective assessment of analytical methods and instrumentation~~, ~~measuring~~ the ~~outcome~~ of ~~a process on a scale~~ of ~~0 to 6. Poor outcomes are measured~~ in ~~terms~~ of ~~defects per million opportunities (DPMO)~~. ~~To do the performance assessment~~ of ~~each clinical laboratory [[analyte]] by Six Sigma analysis and to plan and chart out a better, customized [[quality control]]~~ (QC) ~~plan for each analyte~~, ~~according to its own sigma value~~ ... ('''[[Journal:~~Sigma metrics as a valuable tool for effective analytical performance and~~ quality control ~~planning~~ in ~~the clinical laboratory~~: A ~~retrospective~~ study\|Full article...]]''')<br />		The [[clinical laboratory]] holds a central position in patient care, and as such, ensuring accurate [[laboratory]] test results is a necessity. Internal [[quality control]] (QC) ensures day-to-day laboratory consistency. However, unless practiced, the success of laboratory [[quality management system]]s (QMSs) cannot be achieved. This depends on the efforts and commitment of laboratory personnel for its implementation. Hence, the aim of this study was to find out the knowledge of internal QC for laboratory tests among laboratory personnel working in the Department of Biochemistry, B.P. Koirala Institute of Health Sciences (BPKIHS), a tertiary care center ... ('''[[Journal:Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study\|Full article...]]''')<br />
	''Recently featured'':		''Recently featured'':
	{{flowlist \|		{{flowlist \|
			* [[Journal:Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study\|Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study]]
	* [[Journal:Why do we need food systems informatics? Introduction to this special collection on smart and connected regional food systems\|Why do we need food systems informatics? Introduction to this special collection on smart and connected regional food systems]]		* [[Journal:Why do we need food systems informatics? Introduction to this special collection on smart and connected regional food systems\|Why do we need food systems informatics? Introduction to this special collection on smart and connected regional food systems]]
	* [[Journal:Data management challenges for artificial intelligence in plant and agricultural research\|Data management challenges for artificial intelligence in plant and agricultural research]]		* [[Journal:Data management challenges for artificial intelligence in plant and agricultural research\|Data management challenges for artificial intelligence in plant and agricultural research]]
	* [[Journal:A blockchain-driven IoT-based food quality traceability system for dairy products using a deep learning model\|A blockchain-driven IoT-based food quality traceability system for dairy products using a deep learning model]]
	}}		}}

Shawndouglas: Updated article of the week text

2024-04-29T16:52:29Z

Updated article of the week text

← Older revision		Revision as of 16:52, 29 April 2024
Line 1:		Line 1:
	<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 ~~Tomich Sustain23 15~~-8.png\|260px]]</div>		<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Karaattuthazhathu NatJLabMed23 12-2.png\|260px]]</div>
	'''"[[Journal:~~Why do we need food systems informatics? Introduction to this special collection on smart~~ and ~~connected regional food systems~~\|~~Why do we need food systems informatics? Introduction to this special collection on smart~~ and ~~connected regional food systems~~]]"'''		'''"[[Journal:Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study\|Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study]]"'''

	~~Public interest in where food comes from~~ and ~~how it is produced~~, ~~processed, and distributed has increased over the last few decades, with even greater focus emerging during the~~ [[~~COVID-19~~]] [[~~pandemic~~]]. ~~Mounting evidence~~ and ~~experience point to disturbing weaknesses~~ in ~~our food systems’ abilities to support human livelihoods~~ and ~~wellbeing~~, ~~and alarming long-term trends regarding both~~ the ~~environmental footprint~~ of ~~food systems and mounting vulnerabilities~~ to ~~shocks and stressors~~. ~~How can we tackle~~ the ~~“wicked problems” embedded in a food system? More specifically, how can convergent research programs be designed~~ and ~~resulting knowledge implemented~~ to ~~increase inclusion~~, ~~sustainability~~, ~~and resilience within these complex systems~~ ... ('''[[Journal:~~Why do we need food systems informatics? Introduction to this special collection on smart~~ and ~~connected regional food systems~~\|Full article...]]''')<br />		For the release of precise and accurate reports of [[Medical test\|routine tests]], its necessary to follow a proper [[quality management system]] (QMS) in the [[clinical laboratory]]. As one of the most popular QMS tools for process improvement, Six Sigma techniques and tools have been accepted widely in the [[laboratory]] testing process. Six Sigma gives an objective assessment of analytical methods and instrumentation, measuring the outcome of a process on a scale of 0 to 6. Poor outcomes are measured in terms of defects per million opportunities (DPMO). To do the performance assessment of each clinical laboratory [[analyte]] by Six Sigma analysis and to plan and chart out a better, customized [[quality control]] (QC) plan for each analyte, according to its own sigma value ... ('''[[Journal:Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study\|Full article...]]''')<br />
	''Recently featured'':		''Recently featured'':
	{{flowlist \|		{{flowlist \|
			* [[Journal:Why do we need food systems informatics? Introduction to this special collection on smart and connected regional food systems\|Why do we need food systems informatics? Introduction to this special collection on smart and connected regional food systems]]
	* [[Journal:Data management challenges for artificial intelligence in plant and agricultural research\|Data management challenges for artificial intelligence in plant and agricultural research]]		* [[Journal:Data management challenges for artificial intelligence in plant and agricultural research\|Data management challenges for artificial intelligence in plant and agricultural research]]
	* [[Journal:A blockchain-driven IoT-based food quality traceability system for dairy products using a deep learning model\|A blockchain-driven IoT-based food quality traceability system for dairy products using a deep learning model]]		* [[Journal:A blockchain-driven IoT-based food quality traceability system for dairy products using a deep learning model\|A blockchain-driven IoT-based food quality traceability system for dairy products using a deep learning model]]
	* [[Journal:Effect of good clinical laboratory practices (GCLP) quality training on knowledge, attitude, and practice among laboratory professionals: Quasi-experimental study\|Effect of good clinical laboratory practices (GCLP) quality training on knowledge, attitude, and practice among laboratory professionals: Quasi-experimental study]]
	}}		}}

Shawndouglas: Updated article of the week text

2024-04-22T17:11:54Z

Updated article of the week text

← Older revision		Revision as of 17:11, 22 April 2024
Line 1:		Line 1:
	<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:~~Tab1 Williamson F1000Res2023 10~~.png\|~~240px~~]]</div>		<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Tomich Sustain23 15-8.png\|260px]]</div>
	'''"[[Journal:~~Data management challenges for artificial intelligence in plant~~ and ~~agricultural research~~\|~~Data management challenges for artificial intelligence in plant~~ and ~~agricultural research~~]]"'''		'''"[[Journal:Why do we need food systems informatics? Introduction to this special collection on smart and connected regional food systems\|Why do we need food systems informatics? Introduction to this special collection on smart and connected regional food systems]]"'''

	~~[[Artificial intelligence]] (AI) is increasingly used within plant science, yet it is far from being routinely and effectively implemented~~ in ~~this domain. Particularly relevant to the development of novel~~ food and ~~agricultural technologies~~ is ~~the development of validated~~, ~~meaningful~~, and ~~usable ways to integrate~~, ~~compare, and~~ [[~~Data visualization\|visualize~~]] ~~large, multi-dimensional datasets from different sources~~ and ~~scientific approaches. After a brief summary of the reasons for the interest~~ in ~~data science~~ and ~~AI within plant science~~, the ~~paper identifies~~ and ~~discusses eight key challenges in [[Information management\|data management]] that must be addressed~~ to ~~further unlock~~ the ~~potential of AI~~ in ~~crop~~ and ~~agronomic research~~, and ~~particularly the application of [[machine learning]] (ML), which holds much promise for this domain~~ ... ('''[[Journal:~~Data management challenges for artificial intelligence in plant~~ and ~~agricultural research~~\|Full article...]]''')<br />		Public interest in where food comes from and how it is produced, processed, and distributed has increased over the last few decades, with even greater focus emerging during the [[COVID-19]] [[pandemic]]. Mounting evidence and experience point to disturbing weaknesses in our food systems’ abilities to support human livelihoods and wellbeing, and alarming long-term trends regarding both the environmental footprint of food systems and mounting vulnerabilities to shocks and stressors. How can we tackle the “wicked problems” embedded in a food system? More specifically, how can convergent research programs be designed and resulting knowledge implemented to increase inclusion, sustainability, and resilience within these complex systems ... ('''[[Journal:Why do we need food systems informatics? Introduction to this special collection on smart and connected regional food systems\|Full article...]]''')<br />
	''Recently featured'':		''Recently featured'':
	{{flowlist \|		{{flowlist \|
			* [[Journal:Data management challenges for artificial intelligence in plant and agricultural research\|Data management challenges for artificial intelligence in plant and agricultural research]]
	* [[Journal:A blockchain-driven IoT-based food quality traceability system for dairy products using a deep learning model\|A blockchain-driven IoT-based food quality traceability system for dairy products using a deep learning model]]		* [[Journal:A blockchain-driven IoT-based food quality traceability system for dairy products using a deep learning model\|A blockchain-driven IoT-based food quality traceability system for dairy products using a deep learning model]]
	* [[Journal:Effect of good clinical laboratory practices (GCLP) quality training on knowledge, attitude, and practice among laboratory professionals: Quasi-experimental study\|Effect of good clinical laboratory practices (GCLP) quality training on knowledge, attitude, and practice among laboratory professionals: Quasi-experimental study]]		* [[Journal:Effect of good clinical laboratory practices (GCLP) quality training on knowledge, attitude, and practice among laboratory professionals: Quasi-experimental study\|Effect of good clinical laboratory practices (GCLP) quality training on knowledge, attitude, and practice among laboratory professionals: Quasi-experimental study]]
	* [[Journal:GitHub as an open electronic laboratory notebook for real-time sharing of knowledge and collaboration\|GitHub as an open electronic laboratory notebook for real-time sharing of knowledge and collaboration]]
	}}		}}

Shawndouglas: Updated article of the week text

2024-04-15T17:50:45Z

Updated article of the week text

← Older revision		Revision as of 17:50, 15 April 2024
Line 1:		Line 1:
	<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:~~Fig1 Manisha HighConComp2023 3-3~~.~~jpg~~\|240px]]</div>		<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png\|240px]]</div>
	'''"[[Journal:~~A blockchain-driven IoT-based food quality traceability system~~ for ~~dairy products using a deep learning model~~\|~~A blockchain-driven IoT-based food quality traceability system~~ for ~~dairy products using a deep learning model~~]]"'''		'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research\|Data management challenges for artificial intelligence in plant and agricultural research]]"'''

	~~Food~~ [[~~traceability~~]] is ~~a critical factor that can ensure~~ food ~~safety while enhancing~~ the ~~credibility~~ of ~~the manufactured product~~, ~~thus achieving heightened user satisfaction~~ and ~~loyalty~~. ~~The perishable food supply chain (PFSC) requires paramount care~~ for ~~ensuring~~ [[~~Quality (business)~~\|~~quality~~]] ~~owing~~ to the ~~limited product life. The PFSC comprises~~ of ~~multiple organizations with varied interests~~ and ~~is more likely to be hesitant in sharing~~ the ~~traceability details among one another owing to a lack~~ of ~~trust, which can be overcome by using blockchain. In this research, an efficient scheme using a blockchain-enabled deep~~ [[~~wikipedia:Residual neural network\|residual network~~]] (~~BC-DRN~~) ~~is developed to provide food traceability~~ for ~~dairy products. Here, food traceability is determined by using various modular tools~~ ... ('''[[Journal:~~A blockchain-driven IoT-based food quality traceability system~~ for ~~dairy products using a deep learning model~~\|Full article...]]''')<br />		[[Artificial intelligence]] (AI) is increasingly used within plant science, yet it is far from being routinely and effectively implemented in this domain. Particularly relevant to the development of novel food and agricultural technologies is the development of validated, meaningful, and usable ways to integrate, compare, and [[Data visualization\|visualize]] large, multi-dimensional datasets from different sources and scientific approaches. After a brief summary of the reasons for the interest in data science and AI within plant science, the paper identifies and discusses eight key challenges in [[Information management\|data management]] that must be addressed to further unlock the potential of AI in crop and agronomic research, and particularly the application of [[machine learning]] (ML), which holds much promise for this domain ... ('''[[Journal:Data management challenges for artificial intelligence in plant and agricultural research\|Full article...]]''')<br />
	''Recently featured'':		''Recently featured'':
	{{flowlist \|		{{flowlist \|
			* [[Journal:A blockchain-driven IoT-based food quality traceability system for dairy products using a deep learning model\|A blockchain-driven IoT-based food quality traceability system for dairy products using a deep learning model]]
	* [[Journal:Effect of good clinical laboratory practices (GCLP) quality training on knowledge, attitude, and practice among laboratory professionals: Quasi-experimental study\|Effect of good clinical laboratory practices (GCLP) quality training on knowledge, attitude, and practice among laboratory professionals: Quasi-experimental study]]		* [[Journal:Effect of good clinical laboratory practices (GCLP) quality training on knowledge, attitude, and practice among laboratory professionals: Quasi-experimental study\|Effect of good clinical laboratory practices (GCLP) quality training on knowledge, attitude, and practice among laboratory professionals: Quasi-experimental study]]
	* [[Journal:GitHub as an open electronic laboratory notebook for real-time sharing of knowledge and collaboration\|GitHub as an open electronic laboratory notebook for real-time sharing of knowledge and collaboration]]		* [[Journal:GitHub as an open electronic laboratory notebook for real-time sharing of knowledge and collaboration\|GitHub as an open electronic laboratory notebook for real-time sharing of knowledge and collaboration]]
	* [[Journal:SODAR: Managing multiomics study data and metadata\|SODAR: Managing multiomics study data and metadata]]
	}}		}}

← Older revision		Revision as of 15:05, 17 June 2024
Line 1:		Line 1:
	<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:~~Fig2 Berezin PLoSCompBio23 19~~-12.~~png~~\|240px]]</div>		<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Soto-Perdomo SoftwareX2023 24.jpg\|240px]]</div>
	'''"[[Journal:~~Ten simple rules~~ for ~~managing laboratory information~~\|~~Ten simple rules~~ for ~~managing laboratory information~~]]"'''		'''"[[Journal:OptiGUI DataCollector: A graphical user interface for automating the data collecting process in optical and photonics labs\|OptiGUI DataCollector: A graphical user interface for automating the data collecting process in optical and photonics labs]]"'''

	~~[[Information]]~~ is ~~the cornerstone of [[research]], from experimental~~ data~~/[[metadata]]~~ and ~~computational processes to complex inventories of reagents~~ and equipment. ~~These 10 simple rules discuss best practices~~ for ~~leveraging~~ [[~~laboratory information management system~~]]s ~~(LIMS) to transform this large information load into useful scientific findings~~. ~~The development of [[mathematical model]]s~~ that ~~can predict the properties of biological systems is the holy grail~~ of [[~~computational biology~~]]. ~~Such models can be used to test biological hypotheses, guide the development of biomanufactured products, engineer new systems meeting user-defined specifications, and much more~~ ... ('''[[Journal:~~Ten simple rules~~ for ~~managing laboratory information~~\|Full article...]]''')<br />		OptiGUI DataCollector is a Python 3.8-based graphical user interface (GUI) that facilitates automated data collection in optics and photonics research and development equipment. It provides an intuitive and easy-to-use platform for controlling a wide range of optical instruments, including [[spectrometer]]s and lasers. OptiGUI DataCollector is a flexible and modular framework that enables simple integration with different types of devices. It simplifies experimental workflow and reduces human error by automating parameter control, data acquisition, and [[Data analysis\|analysis]]. OptiGUI DataCollector is currently focused on optical mode conversion utilizing fiber optic technologies ... ('''[[Journal:OptiGUI DataCollector: A graphical user interface for automating the data collecting process in optical and photonics labs\|Full article...]]''')<br />

	''Recently featured'':		''Recently featured'':
	{{flowlist \|		{{flowlist \|
			* [[Journal:Ten simple rules for managing laboratory information\|Ten simple rules for managing laboratory information]]
	* [[Journal:Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology\|Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology]]		* [[Journal:Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology\|Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology]]
	* [[Journal:Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study\|Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study]]		* [[Journal:Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study\|Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study]]
	* [[Journal:Judgements of research co-created by generative AI: Experimental evidence\|Judgements of research co-created by generative AI: Experimental evidence]]
	}}		}}

← Older revision		Revision as of 18:03, 10 June 2024
Line 1:		Line 1:
	<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:~~Fig1 Signoroni NatComm23 14~~.png\|240px]]</div>		<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Berezin PLoSCompBio23 19-12.png\|240px]]</div>
	'''"[[Journal:~~Hierarchical AI enables global interpretation~~ of ~~culture plates in~~ the ~~era~~ of ~~digital microbiology\|Hierarchical AI enables global interpretation~~ of ~~culture plates in~~ the ~~era~~ of ~~digital microbiology~~]]"'''		'''"[[Journal:Ten simple rules for managing laboratory information\|Ten simple rules for managing laboratory information]]"'''

			[[Information]] is the cornerstone of [[research]], from experimental data/[[metadata]] and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging [[laboratory information management system]]s (LIMS) to transform this large information load into useful scientific findings. The development of [[mathematical model]]s that can predict the properties of biological systems is the holy grail of [[computational biology]]. Such models can be used to test biological hypotheses, guide the development of biomanufactured products, engineer new systems meeting user-defined specifications, and much more ... ('''[[Journal:Ten simple rules for managing laboratory information\|Full article...]]''')<br />

	Full [[laboratory automation]] is revolutionizing work habits in an increasing number of clinical [[microbiology]] facilities worldwide, generating huge streams of [[Imaging\|digital images]] for interpretation. Contextually, [[deep learning]] (DL) architectures are leading to paradigm shifts in the way computers can assist with difficult visual interpretation tasks in several domains. At the crossroads of these epochal trends, we present a system able to tackle a core task in clinical microbiology, namely the global interpretation of diagnostic [[Bacteria\|bacterial]] [[Cell culture\|culture]] plates, including presumptive [[pathogen]] identification. This is achieved by decomposing the problem into a hierarchy of complex subtasks and addressing them with a multi-network architecture we call DeepColony ... ('''[[Journal:Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology\|Full article...]]''')<br />
	''Recently featured'':		''Recently featured'':
	{{flowlist \|		{{flowlist \|
			* [[Journal:Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology\|Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology]]
	* [[Journal:Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study\|Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study]]		* [[Journal:Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study\|Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study]]
	* [[Journal:Judgements of research co-created by generative AI: Experimental evidence\|Judgements of research co-created by generative AI: Experimental evidence]]		* [[Journal:Judgements of research co-created by generative AI: Experimental evidence\|Judgements of research co-created by generative AI: Experimental evidence]]
	* [[Journal:Geochemical biodegraded oil classification using a machine learning approach\|Geochemical biodegraded oil classification using a machine learning approach]]
	}}		}}

← Older revision		Revision as of 15:02, 3 June 2024
Line 1:		Line 1:
	<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 ~~Čartolovni DigitalHealth2023 9~~.~~jpeg~~\|240px]]</div>		<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Signoroni NatComm23 14.png\|240px]]</div>
	'''"[[Journal:~~Critical analysis~~ of the ~~impact~~ of AI ~~on the patient–physician relationship: A multi-stakeholder qualitative study\|Critical analysis~~ of the ~~impact~~ of ~~AI on the patient–physician relationship: A multi-stakeholder qualitative study~~]]"'''		'''"[[Journal:Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology\|Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology]]"'''

	~~This qualitative study aims to present the aspirations, expectations~~, ~~and critical analysis~~ of ~~the potential~~ for [[~~artificial intelligence~~]] (AI) to ~~transform~~ the ~~patient–physician relationship, according to multi-stakeholder insight~~. ~~This study was conducted from June to December 2021, using an anticipatory ethics approach and sociology of expectations as~~ the ~~theoretical frameworks. It focused mainly on three groups~~ of ~~stakeholders~~, namely ~~physicians (''n'' = 12), patients (''n'' = 15), and healthcare managers (''n'' = 11), all of whom are directly related to~~ the ~~adoption~~ of ~~AI in medicine (''n'' = 38)~~. ~~In this study, interviews were conducted with 40% of the patients in~~ the ~~sample (15/38), as well as 31%~~ of ~~the physicians (12/38)~~ and ~~29% of health managers in the sample (11/38)~~ ... ('''[[Journal:~~Critical analysis~~ of the ~~impact~~ of ~~AI on the patient–physician relationship: A multi-stakeholder qualitative study~~\|Full article...]]''')<br />		Full [[laboratory automation]] is revolutionizing work habits in an increasing number of clinical [[microbiology]] facilities worldwide, generating huge streams of [[Imaging\|digital images]] for interpretation. Contextually, [[deep learning]] (DL) architectures are leading to paradigm shifts in the way computers can assist with difficult visual interpretation tasks in several domains. At the crossroads of these epochal trends, we present a system able to tackle a core task in clinical microbiology, namely the global interpretation of diagnostic [[Bacteria\|bacterial]] [[Cell culture\|culture]] plates, including presumptive [[pathogen]] identification. This is achieved by decomposing the problem into a hierarchy of complex subtasks and addressing them with a multi-network architecture we call DeepColony ... ('''[[Journal:Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology\|Full article...]]''')<br />
	''Recently featured'':		''Recently featured'':
	{{flowlist \|		{{flowlist \|
			* [[Journal:Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study\|Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study]]
	* [[Journal:Judgements of research co-created by generative AI: Experimental evidence\|Judgements of research co-created by generative AI: Experimental evidence]]		* [[Journal:Judgements of research co-created by generative AI: Experimental evidence\|Judgements of research co-created by generative AI: Experimental evidence]]
	* [[Journal:Geochemical biodegraded oil classification using a machine learning approach\|Geochemical biodegraded oil classification using a machine learning approach]]		* [[Journal:Geochemical biodegraded oil classification using a machine learning approach\|Geochemical biodegraded oil classification using a machine learning approach]]
	* [[Journal:Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study\|Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study]]
	}}		}}

← Older revision		Revision as of 15:48, 26 May 2024
Line 1:		Line 1:
	<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 ~~Niszczota EconBusRev23~~ 9-2.~~png~~\|240px]]</div>		<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Čartolovni DigitalHealth2023 9.jpeg\|240px]]</div>
	'''"[[Journal:~~Judgements~~ of ~~research co-created by generative~~ AI: ~~Experimental evidence~~\|~~Judgements~~ of ~~research co-created by generative~~ AI: ~~Experimental evidence~~]]"'''		'''"[[Journal:Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study\|Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study]]"'''

	~~The introduction~~ of ~~[[ChatGPT]] has fuelled a public debate on~~ the ~~appropriateness of using generative~~ [[artificial intelligence]] (AI) (~~[[large language model]]s or LLMs~~) ~~in work~~, ~~including a debate on how they might be used~~ (~~and abused~~) ~~by researchers. In the current work~~, ~~we test whether delegating parts of the research process to LLMs leads people to distrust researchers~~ and ~~devalues their scientific work. Participants~~ (''N'' = ~~402~~) ~~considered a researcher who delegates elements~~ of the ~~research process to a PhD student or LLM and rated three aspects~~ of ~~such delegation~~. ~~Firstly~~, ~~they rated whether it is morally appropriate to do so. Secondly~~, ~~they judged whether—after deciding to delegate~~ the ~~research process—they would trust~~ the ~~scientist~~ (~~who decided to delegate~~) ~~to oversee future projects~~ ... ('''[[Journal:~~Judgements~~ of ~~research co-created by generative~~ AI: ~~Experimental evidence~~\|Full article...]]''')<br />		This qualitative study aims to present the aspirations, expectations, and critical analysis of the potential for [[artificial intelligence]] (AI) to transform the patient–physician relationship, according to multi-stakeholder insight. This study was conducted from June to December 2021, using an anticipatory ethics approach and sociology of expectations as the theoretical frameworks. It focused mainly on three groups of stakeholders, namely physicians (''n'' = 12), patients (''n'' = 15), and healthcare managers (''n'' = 11), all of whom are directly related to the adoption of AI in medicine (''n'' = 38). In this study, interviews were conducted with 40% of the patients in the sample (15/38), as well as 31% of the physicians (12/38) and 29% of health managers in the sample (11/38) ... ('''[[Journal:Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study\|Full article...]]''')<br />
	''Recently featured'':		''Recently featured'':
	{{flowlist \|		{{flowlist \|
			* [[Journal:Judgements of research co-created by generative AI: Experimental evidence\|Judgements of research co-created by generative AI: Experimental evidence]]
	* [[Journal:Geochemical biodegraded oil classification using a machine learning approach\|Geochemical biodegraded oil classification using a machine learning approach]]		* [[Journal:Geochemical biodegraded oil classification using a machine learning approach\|Geochemical biodegraded oil classification using a machine learning approach]]
	* [[Journal:Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study\|Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study]]		* [[Journal:Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study\|Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study]]
	* [[Journal:Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study\|Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study]]
	}}		}}

← Older revision		Revision as of 15:26, 20 May 2024
Line 1:		Line 1:
	<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 ~~Bispo~~-~~Silva Geosciences23 13-11~~.png\|240px]]</div>		<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Niszczota EconBusRev23 9-2.png\|240px]]</div>
	'''"[[Journal:~~Geochemical biodegraded oil classification using a machine learning approach~~\|~~Geochemical biodegraded oil classification using a machine learning approach~~]]"'''		'''"[[Journal:Judgements of research co-created by generative AI: Experimental evidence\|Judgements of research co-created by generative AI: Experimental evidence]]"'''

	[[~~Chromatography\|Chromatographic~~]] ~~oil analysis is an important step for~~ the identification of biodegraded petroleum via peak visualization and interpretation of phenomena that explain the oil geochemistry. However, analyses of chromatogram components by geochemists are comparative, visual, and consequently slow. This article aims to improve the chromatogram analysis process performed during geochemical interpretation by proposing the use of [[~~convolutional neural network~~]]s ~~(CNN~~), ~~which are deep learning techniques widely~~ used by ~~big tech companies~~. ~~Two hundred~~ and ~~twenty-one~~ (~~221~~) ~~chromatographic oil images from different worldwide basins (Brazil, USA, Portugal, Angola,~~ and ~~Venezuela) were used~~. ~~The [[open-source software]] Orange Data Mining was used~~ to ~~process images by CNN~~. ~~The CNN algorithm extracts~~, ~~pixel by pixel, recurring features from~~ the ~~images through convolutional operations~~ ... ('''[[Journal:~~Geochemical biodegraded oil classification using a machine learning approach~~\|Full article...]]''')<br />		The introduction of [[ChatGPT]] has fuelled a public debate on the appropriateness of using generative [[artificial intelligence]] (AI) ([[large language model]]s or LLMs) in work, including a debate on how they might be used (and abused) by researchers. In the current work, we test whether delegating parts of the research process to LLMs leads people to distrust researchers and devalues their scientific work. Participants (''N'' = 402) considered a researcher who delegates elements of the research process to a PhD student or LLM and rated three aspects of such delegation. Firstly, they rated whether it is morally appropriate to do so. Secondly, they judged whether—after deciding to delegate the research process—they would trust the scientist (who decided to delegate) to oversee future projects ... ('''[[Journal:Judgements of research co-created by generative AI: Experimental evidence\|Full article...]]''')<br />
	''Recently featured'':		''Recently featured'':
	{{flowlist \|		{{flowlist \|
			* [[Journal:Geochemical biodegraded oil classification using a machine learning approach\|Geochemical biodegraded oil classification using a machine learning approach]]
	* [[Journal:Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study\|Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study]]		* [[Journal:Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study\|Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study]]
	* [[Journal:Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study\|Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study]]		* [[Journal:Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study\|Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study]]
	* [[Journal:Why do we need food systems informatics? Introduction to this special collection on smart and connected regional food systems\|Why do we need food systems informatics? Introduction to this special collection on smart and connected regional food systems]]
	}}		}}