Tb3+ assisted dithioerythritol stabilized copper nanocluster with AIE behavior for ratiometric fluorescent determination of fluoroquinolones.

BACKGROUND: Fluoroquinolones (FQs) are widely used in livestock and poultry industry because of their satisfactory effects in preventing and treating bacterial infection. However, due to irrational use and poor biodegradability, FQs can easily remain in food animals and further enter the human body through the food chain. Therefore, accurate and sensitive detection of FQs residues in animal-origin food is significant. The traditional methods commonly used for FQs detection have some limitations. Ratiometric fluorescence detection technology has the advantages of fast, sensitive, self-correcting, and easy visualization. However, the reports on the use of ratiometric fluorescence probes for FQs detection are limited. RESULTS: In this work, a novel probe was proposed for ratiometric fluorescent analysis of FQs. In this probe, the fluorescence of dithioerythritol stabilized copper nanoclusters (DTE-Cu NCs) was significantly enhanced due to the Tb3+ triggered aggregation-induced emission effect. FQs bound Tb3+ in Tb3+/DTE-Cu NCs through carboxyl and carbonyl groups, so that Tb3+ was effectively sensitized to emit green fluorescence. However, the red fluorescence of DTE-Cu NCs was not interfered. The fluorescence of the probe transformed from red to green with the increase of FQs concentration. Using norfloxacin (NOR), difloxacin (DIF), and enrofloxacin (ENR) as FQs simulants, this probe showed a sensitive linear response ranged from 0.025 to 22.5 µM, with the limits of detection of 9.6 nM, 9.3 nM, and 7.7 nM. The application potential for FQs detection was verified via a standard addition assay of egg samples with the recovery rate of 90.4 %-114.7 %. SIGNIFICANT: The fluorescence probe based on Tb3+/DTE-Cu NCs is expected to realize the ratiometric fluorescence sensitive detection of FQs. The establishment of this simple, effective, and rapid detection platform opens up a new way for the detection of FQs residues in animal-origin foods, and also provides a new idea for the design of rapid detection platforms for other hazard factors.

Assessing the distortions introduced when calculating d': A simulation approach.

The discriminability measure d ' is widely used in psychology to estimate sensitivity independently of response bias. The conventional approach to estimate d ' involves a transformation from the hit rate and the false-alarm rate. When performance is perfect, correction methods must be applied to calculate d ' , but these corrections distort the estimate. In three simulation studies, we show that distortion in d ' estimation can arise from other properties of the experimental design (number of trials, sample size, sample variance, task difficulty) that, when combined with application of the correction method, make d ' distortion in any specific experiment design complex and can mislead statistical inference in the worst cases (Type I and Type II errors). To address this problem, we propose that researchers simulate d ' estimation to explore the impact of design choices, given anticipated or observed data. An R Shiny application is introduced that estimates d ' distortion, providing researchers the means to identify distortion and take steps to minimize its impact.

Perceptual decoupling in the sustained attention to response task is unlikely.

Researchers dispute the cause of errors in high Go, low No Go target detection tasks, like the Sustained Attention to Response Task (SART). Some researchers propose errors in the SART are due to perceptual decoupling, where a participant is unaware of stimulus identity. This lack of external awareness causes an erroneous response. Other researchers suggest the majority of the errors in the SART are instead due to response leniency, not perceptual decoupling. Response delays may enable a participant who is initially unaware of stimulus identity, perceptually decoupled, to become aware of stimulus identity, or perceptually recoupled. If, however, the stimulus presentation time is shortened to the minimum necessary for stimulus recognition and the stimulus is disrupted with a structured mask, then there should be no time to enable perception to recouple even with a response delay. From the perceptual decoupling perspective, there should be no impact of a response delay on performance in this case. Alternatively if response bias is critical, then even in this case a response delay may impact performance. In this study, we shortened stimulus presentation time and added a structured mask. We examined whether a response delay impacted performance in the SART and tasks where the SART's response format was reversed. We expected a response delay would only impact signal detection theory bias, c, in the SART, where response leniency is an issue. In the reverse formatted SART, since bias was not expected to be lenient, we expected no impact or minimal impact of a response delay on response bias. These predictions were verified. Response bias is more critical in understanding SART performance, than perceptual decoupling, which is rare if it occurs at all in the SART.

Recommendations for the selection of nucleoside analogues as antihuman herpesvirus drugs: a real-world analysis of reported cases from the FDA adverse event reporting system.

OBJECTIVE: The aim of this study is to provide guidance for refining medication protocols, developing alternative strategies, and enhancing protection against herpesvirus infections in personalized clinical settings. METHODS: Adverse drug events (ADEs) data for anti-herpesvirus from the first quarter of 2004 to the fourth quarter of 2022 were collected from the FDA Adverse Event Reporting System (FAERS). Disproportionality analysis was performed using Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), and Bayesian Confidence Propagation Neural Network (BCPNN) methods for data mining. RESULTS: A total of 18,591, 24,206, 6,150, and 419 reports of ADEs associated with acyclovir (ACV), valacyclovir (VACV), ganciclovir (GCV), and famciclovir (FCV) were screened and extracted from the FAERS. In this study, the report summarized the high frequency and strong correlation of ADEs for the four drugs at the Preferred Term (PT) level. Additionally, the analysis also identified the relationship between ADEs and factors such as age, gender, and severity of outcome at the System Organ Class (SOC) level. CONCLUSION: The safety reports for the four-nucleoside analogue anti-herpesvirus drugs are diverse and interconnected. Dosing for patients with herpesvirus infections should be tailored to their specific conditions and the potential risk of disease.

Clarifying the effects of sequential item presentation in the police lineup task.

Previous research has reported diverging patterns of results with respect to discriminability and response bias when comparing the simultaneous lineup to two different lineup procedures in which items are presented sequentially, the sequential stopping rule lineup and the UK lineup. In a single large sample experiment, we compared discriminability and response bias in six-item photographic lineups presented either simultaneously, sequentially with a stopping rule, or sequentially requiring two full laps through the items before making an identification and including the ability to revisit items, analogous to the UK lineup procedure. Discriminability was greater for the simultaneous lineup compared to the sequential stopping rule lineup, despite a non-significant difference in empirical discriminability between the procedures. There was no significant difference in discriminability when comparing the simultaneous lineup to the sequential two lineup and the sequential two lap lineup to the sequential stopping rule lineup. Responding was most lenient for the sequential two lap lineup, followed by the simultaneous lineup, followed by the sequential lineup. These results imply that sequential item presentation may not exert a large effect in isolation on discriminability and response bias. Rather, discriminability and response bias in the sequential stopping rule lineup and UK lineup result from the interaction of sequential item presentation with other aspects of these procedures.

A real-world data analysis of tirzepatide in the FDA adverse event reporting system (FAERS) database.

Background: Tirzepatide, a glucose-dependent insulinotropic polypeptide (GIP) receptor and glucagon-like peptide-1 (GLP-1) receptor agonist, is indicated for chronic weight management in adults with obesity or overweight as an adjunct to a reduced-calorie diet and increased physical activity. However, the safety profile of Tirzepatide-associated adverse events requires comprehensive evaluation. Methods: The AE reports from the first quarter of 2022 to the third quarter of 2023 were selected by exploring the FDA Adverse Event Reporting System (FAERS) database. The new and unexpected potenial AE signals were detected using the disproportionality analysis, including reporting odds ratio(ROR), the proportional reporting ratio (PRR) the Bayesian confidence propagation neural network (BCPNN) and the empirical Bayes geometric mean(EBGM). Then the MedDRA was used to systematically classify the results. Results: A total of 1,904,481 case reports were obtained from 2022Q2 to 2023Q3. Forty-sixth tirzepatide-induced ADRs at the preferred terms (PTs) level are associated with 8 system organ class In addition, this study uncovered multiple anticipated ADRs, such as gastrooesophageal reflux disease, dyspepsia, and vomiting, in line with the drug labels. Moreover, unexpected and significant ADRs at PTs level, such as incorrect dose administered, injection site haemorrhage, and increased appetite, were discovered and linked to Injury, poisoning, and procedural complications, General disorders and administration site conditions, and Metabolism and nutrition disorders at the System Organ Class level. Conclusion: This study offered new perspectives on the monitoring, surveillance, and management of adverse drug reactions related to tirzepatide. The outcomes of severe adverse events and their respective detection signals, along with unexpected significant adverse event signals, are important to consider in efforts to enhance clinical medication safety when using tirzepatide.

The evolving role of disproportionality analysis in pharmacovigilance.

INTRODUCTION: From 2009 to 2015, the IMI PROTECT conducted rigorous studies addressing questions about optimal implementation and significance of disproportionality analyses, leading to the development of Good Signal Detection Practices. The ensuing period witnessed the independent exploration of research paths proposed by IMI PROTECT, accumulating valuable experience and insights that have yet to be seamlessly integrated. AREAS COVERED: This state-of-the-art review integrates IMI PROTECT recommendations with recent acquisitions and evolving challenges. It deals with defining the object of study, disproportionality methods, subgrouping, masking, drug-drug interaction, duplication, expectedness, the debated use of disproportionality results as risk measures, integration with other types of data. EXPERT OPINION: Despite the ongoing skepticism regarding the usefulness of disproportionality analyses and individual case safety reports, their ability to timely detect safety signals regarding rare and unpredictable adverse reactions remains unparalleled. Moreover, recent exploration into their potential for characterizing safety signals revealed valuable insights concerning potential risk factors and the patient's perspective. To fully realize their potential beyond hypothesis generation and achieve a comprehensive evidence synthesis with other kinds of data and studies, each with their unique limitations and contributions, we need to investigate methods for more transparently communicating disproportionality results and mapping and addressing pharmacovigilance biases.

Monitoring Adverse Drug Events in Web Forums: Evaluation of a Pipeline and Use Case Study.

BACKGROUND: To mitigate safety concerns, regulatory agencies must make informed decisions regarding drug usage and adverse drug events (ADEs). The primary pharmacovigilance data stem from spontaneous reports by health care professionals. However, underreporting poses a notable challenge within the current system. Explorations into alternative sources, including electronic patient records and social media, have been undertaken. Nevertheless, social media's potential remains largely untapped in real-world scenarios. OBJECTIVE: The challenge faced by regulatory agencies in using social media is primarily attributed to the absence of suitable tools to support decision makers. An effective tool should enable access to information via a graphical user interface, presenting data in a user-friendly manner rather than in their raw form. This interface should offer various visualization options, empowering users to choose representations that best convey the data and facilitate informed decision-making. Thus, this study aims to assess the potential of integrating social media into pharmacovigilance and enhancing decision-making with this novel data source. To achieve this, our objective was to develop and assess a pipeline that processes data from the extraction of web forum posts to the generation of indicators and alerts within a visual and interactive environment. The goal was to create a user-friendly tool that enables regulatory authorities to make better-informed decisions effectively. METHODS: To enhance pharmacovigilance efforts, we have devised a pipeline comprising 4 distinct modules, each independently editable, aimed at efficiently analyzing health-related French web forums. These modules were (1) web forums' posts extraction, (2) web forums' posts annotation, (3) statistics and signal detection algorithm, and (4) a graphical user interface (GUI). We showcase the efficacy of the GUI through an illustrative case study involving the introduction of the new formula of Levothyrox in France. This event led to a surge in reports to the French regulatory authority. RESULTS: Between January 1, 2017, and February 28, 2021, a total of 2,081,296 posts were extracted from 23 French web forums. These posts contained 437,192 normalized drug-ADE couples, annotated with the Anatomical Therapeutic Chemical (ATC) Classification and Medical Dictionary for Regulatory Activities (MedDRA). The analysis of the Levothyrox new formula revealed a notable pattern. In August 2017, there was a sharp increase in posts related to this medication on social media platforms, which coincided with a substantial uptick in reports submitted by patients to the national regulatory authority during the same period. CONCLUSIONS: We demonstrated that conducting quantitative analysis using the GUI is straightforward and requires no coding. The results aligned with prior research and also offered potential insights into drug-related matters. Our hypothesis received partial confirmation because the final users were not involved in the evaluation process. Further studies, concentrating on ergonomics and the impact on professionals within regulatory agencies, are imperative for future research endeavors. We emphasized the versatility of our approach and the seamless interoperability between different modules over the performance of individual modules. Specifically, the annotation module was integrated early in the development process and could undergo substantial enhancement by leveraging contemporary techniques rooted in the Transformers architecture. Our pipeline holds potential applications in health surveillance by regulatory agencies or pharmaceutical companies, aiding in the identification of safety concerns. Moreover, it could be used by research teams for retrospective analysis of events.

Automated System for Attomolar-Level Detection of MiRNA as a Biomarker for Influenza A Virus.

We have developed an automated sensing system for the repeated detection of a specific microRNA (miRNA) of the influenza A (H1N1) virus. In this work, magnetic particles functionalized with DNAs, target miRNAs, and alkaline phosphate (ALP) enzymes formed sandwich structures. These particles were trapped on nickel (Ni) patterns of our sensor chip by an external magnetic field. Then, additional electrical signals from electrochemical markers generated by ALP enzymes were measured using the sensor, enabling the highly sensitive detection of target miRNA. The magnetic particles used on the sensor were easily removed by applying the opposite direction of external magnetic fields, which allowed us to repeat sensing measurements. As a proof of concept, we demonstrated the detection of miRNA-1254, one of the biomarkers for the H1N1 virus, with a high sensitivity down to 1 aM in real time. Moreover, our sensor could selectively detect the target from other miRNA samples. Importantly, our sensor chip showed reliable electrical signals even after six repeated miRNA sensing measurements. Furthermore, we achieved technical advances to utilize our sensor platform as part of an automated sensing system. In this regard, our reusable sensing platform could be utilized for versatile applications in the field of miRNA detection and basic research.

A Bayesian method to detect drug-drug interaction using external information for spontaneous reporting system.

Due to the insufficiency of safety assessments of clinical trials for drugs, further assessments are required for post-marketed drugs. In addition to adverse drug reactions (ADRs) induced by one drug, drug-drug interaction (DDI)-induced ADR should also be investigated. The spontaneous reporting system (SRS) is a powerful tool for evaluating the safety of drugs continually. In this study, we propose a novel Bayesian method for detecting potential DDIs in a database collected by the SRS. By applying a power prior, the proposed method can borrow information from similar drugs for a drug assessed DDI to increase sensitivity of detection. The proposed method can also adjust the amount of the information borrowed by tuning the parameters in power prior. In the simulation study, we demonstrate the aforementioned increase in sensitivity. Depending on the scenarios, approximately 20 points of sensitivity of the proposed method increase from an existing method to a maximum. We also indicate the possibility of early detection of potential DDIs by the proposed method through analysis of the database shared by the Food and Drug Administration. In conclusion, the proposed method has a higher sensitivity and a novel criterion to detect potential DDIs early, provided similar drugs have similar observed-expected ratios to the drug under assessment.

Recognition of dynamic facial expressions of emotions in forensic inpatients who have committed sexual offenses: a signal detection analysis.

Emotion recognition is central in prosocial interaction, enabling the inference of mental and affective states. Individuals who have committed sexual offenses are known to exhibit socio-affective deficits, one of the four dynamic risk assessment dimensions found in the literature. Few research focused on emotion recognition. The available literature, exclusively on individuals in prison who have committed sexual offenses, showed contrasting results. Some found a global (across all emotions) or specific (e.g., anger, fear) deficit in emotion recognition. In contrast, others found no difference between individuals in prison who have committed sexual offenses and those who have committed non-sexual offenses. In addition, no such study has been undertaken among forensic inpatients who exhibit socio-affective deficits. This study aims to investigate the recognition of dynamic facial expressions of emotion in 112 male participants divided into three groups: forensic inpatients who have committed sexual offenses (n = 37), forensic inpatients who have committed non-sexual offenses (n = 25), and community members (n = 50), using the Signal Detection Theory indices: sensitivity (d') and response bias (c). In addition, measures related to reaction time, emotion labeling reflection time, task easiness, and easiness reflection time were also collected. Non-parametric analyses (Kruskall-Wallis' H, followed by Mann-Whitney's U with Dunn-Bonferroni correction) highlighted that the two forensic inpatient groups exhibited emotion recognition deficits when compared to community members. Forensic inpatients who have committed sexual offenses were more conservative in selecting the surprise label than community members. They also took significantly more time to react to stimuli and to select an emotional label. Despite emotion recognition deficits, the two forensic inpatient groups reported more stimuli easiness than community members.

Comprehensive signal detection of delirium-associated medication using the Food and Drug Administration Adverse Event Reporting System.

OBJECTIVE: Several medications are associated with delirium; however, studies with adequate statistical power are limited, and it is difficult to determine the effects of the various concomitant medications used in clinical practice. Therefore, in this study, we aimed to comprehensively evaluate the safety signals of delirium-associated drugs using a spontaneous adverse event reporting system. METHOD: The JAPIC AERS (Food and Drug Administration Adverse Event Reporting System pre-processed by the Japan Pharmaceutical Information Center) was used for the analysis in this pharmacovigilance study. The reporting odds ratio (ROR) for delirium was adjusted for using multivariate logistic regression analysis with sex, age, indication, and melatonin receptor agonist use, and 22 drug categories were targeted as covariates. RESULTS: After excluding patients with missing information, 7,527,568 patients were included in the study. Delirium signals were detected even after adjusting for covariates in 17 drug categories, including benzodiazepines (adjusted ROR, 1.76; 95% confidence interval [CI], 1.64-1.89), opioids (adjusted ROR, 4.42; 95% CI, 4.21-4.64), and tricyclic antidepressants (adjusted ROR, 2.44; 95% CI, 2.20-2.71). CONCLUSIONS: These findings suggest that many drug classes, such as benzodiazepines, are independent risk factors for delirium and strengthen the evidence of an association between delirium and medications.

Information perseveration in recognition memory: Examining the scope of sequential dependencies.

Models of recognition memory often assume that decisions are made independently from each other. Yet there is growing evidence that consecutive recognition responses show sequential dependencies, whereby making one response increases the probability of repeating that response from one trial to the next trial. Across six experiments, we replicated this response-related carryover effect using word and nonword stimuli and further demonstrated that the content of the previous trial-both perceptual and conceptual-can also bias the response to the current test probe, with both perceptual (orthographic) and conceptual (semantic) similarity boosting the probability of consecutive "old" responses. Finally, a manipulation of attentional engagement in Experiments 3a and 3b provided little evidence these carryover effects on recognition decisions are merely a product of lapses in attention. Taken together, the current study reinforces prior findings that recognition decisions are not made independently, and that multiple forms of information perseverate across consecutive trials.

Matched Stochastic Resonance Enhanced Underwater Passive Sonar Detection under Non-Gaussian Impulsive Background Noise.

Remote passive sonar detection with low-frequency band spectral lines has attracted much attention, while complex low-frequency non-Gaussian impulsive noisy environments would strongly affect the detection performance. This is a challenging problem in weak signal detection, especially for the high false alarm rate caused by heavy-tailed impulsive noise. In this paper, a novel matched stochastic resonance (MSR)-based weak signal detection model is established, and two MSR-based detectors named MSR-PED and MSR-PSNR are proposed based on a theoretical analysis of the MSR output response. Comprehensive detection performance analyses in both Gasussian and non-Gaussian impulsive noise conditions are presented, which revealed the superior performance of our proposed detector under non-Gasussian impulsive noise. Numerical analysis and application verification have revealed the superior detection performance with the proposed MSR-PSNR detector compared with energy-based detection methods, which can break through the high false alarm rate problem caused by heavy-tailed impulsive noise. For a typical non-Gasussian impulsive noise assumption with α=1.5, the proposed MSR-PED and MSR-PSNR can achieve approximately 16 dB and 22 dB improvements, respectively, in the detection performance compared to the classical PED method. For stronger, non-Gaussian impulsive noise conditions corresponding to α=1, the improvement in detection performance can be more significant. Our proposed MSR-PSNR methods can overcome the challenging problem of a high false alarm rate caused by heavy-tailed impulsive noise. This work can lay a solid foundation for breaking through the challenges of underwater passive sonar detection under non-Gaussian impulsive background noise, and can provide important guidance for future research work.

Reward Sensitivity and Noise Contribute to Negative Affective Bias: A Learning Signal Detection Theory Approach in Decision-Making.

In patients with mood disorders, negative affective biases - systematically prioritising and interpreting information negatively - are common. A translational cognitive task testing this bias has shown that depressed patients have a reduced preference for a high reward under ambiguous decision-making conditions. The precise mechanisms underscoring this bias are, however, not yet understood. We therefore developed a set of measures to probe the underlying source of the behavioural bias by testing its relationship to a participant's reward sensitivity, value sensitivity and reward learning rate. One-hundred-forty-eight participants completed three online behavioural tasks: the original ambiguous-cue decision-making task probing negative affective bias, a probabilistic reward learning task probing reward sensitivity and reward learning rate, and a gambling task probing value sensitivity. We modelled the learning task through a dynamic signal detection theory model and the gambling task through an expectation-maximisation prospect theory model. Reward sensitivity from the probabilistic reward task (ß = 0.131, p = 0.024) and setting noise from the probabilistic reward task (ß = -0.187, p = 0.028) both predicted the affective bias score in a logistic regression. Increased negative affective bias, at least on this specific task, may therefore be driven in part by a combination of reduced sensitivity to rewards and more variable responses.

Abrocitinib-associated adverse events: a real-world pharmacovigilance study using the FAERS database.

BACKGROUND: Atopic dermatitis (AD) is a common chronic inflammatory skin disease. Janus kinase (JAK) modulates cytokines involved in AD pathophysiology, and JAK inhibitors have emerged as effective pharmacotherapeutic remedies for AD. Abrocitinib, an oral selective inhibitor of JAK1, is indicated for the management of moderate-to-severe AD. The current study evaluated the adverse events (AEs) associated with abrocitinib in a real-world setting. METHODS: To quantify the signals of abrocitinib-associated AEs, we used the US Food and Drug Administration Adverse Event Reporting System (FAERS) for this pharmacovigilance study with two established pharmacovigilance methods. RESULTS: A total of 1071 AEs of abrocitinib were investigated as the primary suspected from the FAERS to detect and characterize relevant safety signals. The analysis revealed 85 signals for abrocitinib. The most common AE for abrocitinib was drug ineffective. The signal strength of eczema herpeticum was 515.87 (277.80-957.98) and 510.59 (5148.65) and exhibited the highest strength for abrocitinib. Rare AEs such as aggravated condition, pruritus, and hypersensitivity were not listed on the label, and attention to these AEs is required. CONCLUSION: The analysis of the AE signals may provide support for clinical monitoring and risk identification of abrocitinib.

A canine model to evaluate the effect of exercise intensity and duration on olfactory detection limits: the running nose.

Detection canines serve critical roles to support the military, homeland security and border protection. Some explosive detection tasks are physically demanding for dogs, and prior research suggests this can lead to a reduction in olfactory detection sensitivity. To further evaluate the effect of exercise intensity on olfactory sensitivity, we developed a novel olfactory paradigm that allowed us to measure olfactory detection thresholds while dogs exercised on a treadmill at two different exercise intensities. Dogs (n = 3) showed a decrement in olfactory detection for 1-bromooctane at 10-3 (v/v) dilutions and lower under greater exercise intensity. Dogs' hit rate for the lowest concentration dropped from 0.87 ± 0.04 when walking at low intensity to below 0.45 ± 0.06 when trotting at moderate intensity. This decline had an interaction with the duration of the session in moderate intensity exercise, whereby dogs performed near 100% detection in the first 10 min of the 8 km/h session, but showed 0% detection after 20 min. Hit rates for high odor concentrations (10-2) were relatively stable at both low (1 ± 0.00) and moderate (0.91 ± 0.04) exercise intensities. The paradigm and apparatus developed here may be useful to help further understand causes of operationally relevant olfactory detection threshold decline in dogs.

LED Junction Temperature Measurement: From Steady State to Transient State.

In this review, we meticulously analyze and consolidate various techniques used for measuring the junction temperature of light-emitting diodes (LEDs) by examining recent advancements in the field as reported in the literature. We initiate our exploration by delineating the evolution of LED technology and underscore the criticality of junction temperature detection. Subsequently, we delve into two key facets of LED junction temperature assessment: steady-state and transient measurements. Beginning with an examination of innovations in steady-state junction temperature detection, we cover a spectrum of approaches ranging from traditional one-dimensional methods to more advanced three-dimensional techniques. These include micro-thermocouple, liquid crystal thermography (LCT), temperature sensitive optical parameters (TSOPs), and infrared (IR) thermography methods. We provide a comprehensive summary of the contributions made by researchers in this domain, while also elucidating the merits and demerits of each method. Transitioning to transient detection, we offer a detailed overview of various techniques such as the improved T3ster method, an enhanced one-dimensional continuous rectangular wave method (CRWM), and thermal reflection imaging. Additionally, we introduce novel methods leveraging high-speed camera technology and reflected light intensity (h-SCRLI), as well as micro high-speed transient imaging based on reflected light (µ_HSTI). Finally, we provide a critical appraisal of the advantages and limitations inherent in several transient detection methods and offer prognostications on future developments in this burgeoning field.

A Pilot, Predictive Surveillance Model in Pharmacovigilance Using Machine Learning Approaches.

INTRODUCTION: The identification of a new adverse event (AE) caused by a drug product is one of the key activities in the pharmaceutical industry to ensure the safety profile of a drug product. Machine learning (ML) has the potential to assist with signal detection and supplement traditional pharmacovigilance (PV) surveillance methods. This pilot ML modeling study was designed to detect potential safety signals for two AbbVie products and test the model's capability of detecting safety signals earlier than humans. METHODS: Drug X, a mature product with post-marketing data, and Drug Y, a recently approved drug in another therapeutic area, were selected. Gradient boosting-based ML approaches (e.g., XGBoost) were applied as the main modeling strategy. RESULTS: For Drug X, eight true signals were present in the test set. Among 12 potential new signals generated, four were true signals with a 50.0% sensitivity rate and a 33.3% positive predictive value (PPV) rate. Among the remaining eight potential new signals, one was confirmed as a signal and detected six months earlier than humans. For Drug Y, nine true signals were present in the test set. Among 13 potential new signals generated, five were true signals with a 55.6% sensitivity rate and a 38.5% PPV rate. Among the remaining eight potential new signals, none were confirmed as true signals upon human review. CONCLUSION: This model demonstrated acceptable accuracy for safety signal detection and potential for earlier detection when compared to humans. Expert judgment, flexibility, and critical thinking are essential human skills required for the final, accurate assessment of adverse event cases.

Self-Powered Hybrid Motion and Health Sensing System Based on Triboelectric Nanogenerators.

Triboelectric nanogenerator (TENG) represents an effective approach for the conversion of mechanical energy into electrical energy and has been explored to combine multiple technologies in past years. Self-powered sensors are not only free from the constraints of mechanical energy in the environment but also capable of efficiently harvesting ambient energy to sustain continuous operation. In this review, the remarkable development of TENG-based human body sensing achieved in recent years is presented, with a specific focus on human health sensing solutions, such as body motion and physiological signal detection. The movements originating from different parts of the body, such as body, touch, sound, and eyes, are systematically classified, and a thorough review of sensor structures and materials is conducted. Physiological signal sensors are categorized into non-implantable and implantable biomedical sensors for discussion. Suggestions for future applications of TENG-based biomedical sensors are also indicated, highlighting the associated challenges.