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Mental health disorders are a leading cause of medical disabilities across an individual's lifespan. This burden is particularly substantial in children and adolescents because of challenges in diagnosis and the lack of precision medicine approaches. However, the widespread adoption of wearable devices (eg, smart watches) that are conducive for artificial intelligence applications to remotely diagnose and manage psychiatric disorders in children and adolescents is promising. This study aims to conduct a scoping review to study, characterize, and identify areas of innovations with wearable devices that can augment current in-person physician assessments to individualize diagnosis and management of psychiatric disorders in child and adolescent psychiatry. This scoping review used information from the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A comprehensive search of several databases from 2011 to June 25, 2021, limited to the English language and excluding animal studies, was conducted. The databases included Ovid MEDLINE and Epub ahead of print, in-process and other nonindexed citations, and daily; Ovid Embase; Ovid Cochrane Central Register of Controlled Trials; Ovid Cochrane Database of Systematic Reviews; Web of Science; and Scopus. The initial search yielded 344 articles, from which 19 (5.5%) articles were left on the final source list for this scoping review. Articles were divided into three main groups as follows: studies with the main focus on autism spectrum disorder, attention-deficit/hyperactivity disorder, and internalizing disorders such as anxiety disorders. Most of the studies used either cardio-fitness chest straps with electrocardiogram sensors or wrist-worn biosensors, such as watches by Fitbit. Both allowed passive data collection of the physiological signals. Our scoping review found a large heterogeneity of methods and findings in artificial intelligence studies in child psychiatry. Overall, the largest gap identified in this scoping review is the lack of randomized controlled trials, as most studies available were pilot studies and feasibility trials.

The ability to mix liquids in microchannel networks is fundamentally important in the design of nearly every miniaturized chemical and biochemical analysis system. Here, we show that enhanced micromixing can be achieved in topologically simple and easily fabricated planar 2D microchannels by simply introducing curvature and changes in width in a prescribed manner. This goal is accomplished by harnessing a synergistic combination of (i) Dean vortices that arise in the vertical plane of curved channels as a consequence of an interplay between inertial, centrifugal, and viscous effects, and (ii) expansion vortices that arise in the horizontal plane due to an abrupt increase in a conduit's cross-sectional area. We characterize these effects by using confocal microscopy of aqueous fluorescent dye streams and by observing binding interactions between an intercalating dye and double-stranded DNA. These mixing approaches are versatile and scalable and can be straightforwardly integrated as generic components in a variety of lab-on-a-chip systems.

An assessment of anomalies in the hierarchical organization of the drainage network in the Kuttiyadi River Basin (KuRB), Kerala, has been performed by considering various morphometric parameters such as bifurcation index (R), hierarchical anomaly index (∆a), hierarchical anomaly density (ga), and stream gradient index (SL) in a geographical information system (GIS) platform. Further, a digital elevation model (DEM) of the area has been generated from Cartosat stereo pair data at 2.5-m resolution. The computed quantitative information about drainage characteristics reveals the highest drainage anomaly is observed in sub-watersheds (SW) III and IV. It is observed that neo-tectonic activity caused the development of younger stage drainage patterns of structural controls in the sub-watersheds of this river basin. The tectonic activity-induced diffusion, high energy fluvial erosion, and anthropogenic interferences altered the hierarchical organization of the drainage network of the sub-watersheds in mature to old stages of geomorphic evolution. The results of finding validated with asymmetry factor and ratio of the hierarchical index (∆a) with hierarchical anomaly number (A), bifurcation index (R), direct bifurcation ratio (Rdb), stream gradient index (SL), and denudation index (logTu). From the denudation index analysis, the sediment yield of the river basin is identified as 0.67 t·km−2·yr−1. Moreover, the asymmetric factor (AF) in the KuRB shows the imprints of Paleo—Neo Proterozoic crustal tilting toward a NNW—SSE direction.

Gene functional studies often rely on the expression of a gene of interest as transcriptional and translational fusions with specialized tags. Ideally, this is done in the native chromosomal contexts to avoid potential misexpression artifacts. Although recent improvements in genome editing have made it possible to directly modify the target genes in their native chromosomal locations, classical transgenesis is still the preferred experimental approach chosen in most gene tagging studies because of its time efficiency and accessibility. We have developed a recombineering-based tagging system that brings together the convenience of the classical transgenic approaches and the high degree of confidence in the results obtained by direct chromosomal tagging using genome-editing strategies. These simple, scalable, customizable recombineering toolsets and protocols allow a variety of genetic modifications to be generated. In addition, we developed a highly efficient recombinase-mediated cassette exchange system to facilitate the transfer of the desired sequences from a bacterial artificial chromosome clone to a transformation-compatible binary vector, expanding the use of the recombineering approaches beyond Arabidopsis (Arabidopsis thaliana). We demonstrated the utility of this system by generating more than 250 whole-gene translational fusions and 123 Arabidopsis transgenic lines corresponding to 62 auxin-related genes and characterizing the translational reporter expression patterns for 14 auxin biosynthesis genes.

Decannulation is an essential step in liberating tracheostomised patients from mechanical ventilation. This procedure is purely based on the clinician’s judgment and there is no universally accepted protocol to date for this vital procedure. This study aimed to describe decannulation practice and failure rates in patients with tracheostomy and to determine the factors associated with the outcome of tube removal. A prospective study was done on 50 patients (both sexes) who required a tracheostomy and cared for at Command Hospital Bangalore Center between January 2019 and April 2020. Data were analyzed using descriptive and inferential tests. Out of the 50 decannulation decisions, 7 patients experienced decannulation failures giving a failure rate of 14%. Out of the 7 decannulation failure cases, about 4 patients (10%) experienced difficulty in swallowing and 3 patients (2%) experienced stridor. There was no associated mortality. A decannulation failure of 14% was seen in this study in tracheostomised patients after prolonged mechanical ventilation. Various factors govern the success of tracheostomy decannulation procedures which occur during the first 24–48 h after decannulation. Lack of swallowing/secretions/cough management and the development of stridor were the commonest cause of decannulation failure in this study.

Combination antidepressant pharmacotherapies are frequently used to treat major depressive disorder (MDD). However, there is no evidence that machine learning approaches combining multi-omics measures (e.g., genomics and plasma metabolomics) can achieve clinically meaningful predictions of outcomes to combination pharmacotherapy. This study examined data from 264 MDD outpatients treated with citalopram or escitalopram in the Mayo Clinic Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) and 111 MDD outpatients treated with combination pharmacotherapies in the Combined Medications to Enhance Outcomes of Antidepressant Therapy (CO-MED) study to predict response to combination antidepressant therapies. To assess whether metabolomics with functionally validated single-nucleotide polymorphisms (SNPs) improves predictability over metabolomics alone, models were trained/tested with and without SNPs. Models trained with PGRN-AMPS’ and CO-MED’s escitalopram/citalopram patients predicted response in CO-MED’s combination pharmacotherapy patients with accuracies of 76.6% (p < 0.01; AUC: 0.85) without and 77.5% (p < 0.01; AUC: 0.86) with SNPs. Then, models trained solely with PGRN-AMPS’ escitalopram/citalopram patients predicted response in CO-MED’s combination pharmacotherapy patients with accuracies of 75.3% (p < 0.05; AUC: 0.84) without and 77.5% (p < 0.01; AUC: 0.86) with SNPs, demonstrating cross-trial replication of predictions. Plasma hydroxylated sphingomyelins were prominent predictors of treatment outcomes. To explore the relationship between SNPs and hydroxylated sphingomyelins, we conducted multi-omics integration network analysis. Sphingomyelins clustered with SNPs and metabolites related to monoamine neurotransmission, suggesting a potential functional relationship. These results suggest that integrating specific metabolites and SNPs achieves accurate predictions of treatment response across classes of antidepressants. Finally, these results motivate functional investigation into how sphingomyelins might influence MDD pathophysiology, antidepressant response, or both.

Perovskite solar cells have gained more popularity in recent years because of their high efficiency and low cost. The most widely employed active layer in perovskite solar cells is FAPbI 3 , which possesses higher stability, efficiency, and smaller bandgap value. However, its sensitivity to light and humidity makes it challenging to prepare under normal conditions in an economical manner. In this paper, we tried to control not only humidity during the annealing stage but also light intensity conditions of FAPbI 3  samples. Here, we synthesize samples in four different ways: (i) Without dark annealing; (ii) Vacuum-assisted without dark annealing; (iii) Dark annealing; and (iv) Vacuum-assisted dark annealing. Among these methods, Dark annealing and Vacuum-assisted dark annealing are two effective methods where we controlled light during the annealing stage. The morphological, structural, and optical characteristics of the samples were investigated. The FTIR data showed that the relative humidity of the sample is favourably reduced for the samples prepared using vacuum-assisted methods. X-ray analysis confirmed the α phase of FAPbI 3 , which is a favourable phase for solar cell applications. The FESEM analysis confirmed the defect-free morphology of the prepared sample. The prepared α- FAPbI 3  accounts for a bandgap value of 1.51 eV, which is quite close to the ideal bandgap value. The α phase of FAPbI 3  is also conformed from TEM analysis. Thermal stability of sample was analysed by TGA/DTA and got a decomposition temperature of 442 °C.

We report two cases of tuberculosis meningitis patients developing hemichorea-hemiballismus during antituberculosis treatment. First, a 56-year-old woman experienced right-sided hemichorea-hemiballismus 3 months into treatment. MRI scans revealed a left thalamus and subthalamic infarct. After 10 days of continued treatment and corticosteroids, her movements subsided. Second, a 17-year-old female developed hemichorea-hemiballismus while on antituberculosis drugs and corticosteroids. MRI scans displayed ischemic lesions, optochiasmatic arachnoiditis, gyral enhancement, and a small tuberculoma. After shunt surgery and tetrabenazine treatment, she significantly improved and resumed daily activities. In conclusion, hemichorea-hemiballismus may paradoxically occur in tuberculosis meningitis patients, potentially linked to ischemic lesions in the thalamus and subthalamus.

Background Hepatitis C viral infection is curable, yet only about one-third of identified cases have been cleared nationally. We evaluated determinants of treatment completion in a sample of newly reported hepatitis C cases among Los Angeles County residents. Methods Using information from the Los Angeles County Hepatitis C Case Registry, we contacted reported hepatitis C ribonucleic acid (RNA) positive cases diagnosed from January 2021 through April 2022. We used Pearson’s Chi-Square Tests and multivariable logistic regression to identify demographic and clinical characteristics associated with self-reported hepatitis C virus treatment completion. Results Among 2,992 reported RNA-positive cases, 619 (21%) were successfully contacted. Among those, most respondents were untreated (72%), male (65%), and tested by primary care providers (50%). Individuals 30–44 years old had 2.9 (95% CI: 1.1, 7.4) times the odds of having completed treatment during initial contact compared to 18–29 year-olds, adjusted for ordering provider type, race/ethnicity, health insurance type, and symptom status. Respondents tested by specialists had 1.8 (95% CI: 1.2, 2.8) times the odds of completing treatment compared to those tested by primary care providers. Respondents who reported symptoms in the past six months had 0.2 (95% CI: 0.1, 0.4) times the odds of completing treatment compared to respondents with no symptoms. Characteristics of respondents were the same as those diagnosed with hepatitis C in Los Angeles County. Conclusions We verified low hepatitis C treatment completion at the population-level. Those 18–29 years old and diagnosed in primary care were least likely to complete treatment.

Background When job demand exceeds job resources, burnout occurs. Burnout in healthcare workers extends beyond negatively affecting their functioning and physical and mental health; it also has been associated with poor medical outcomes for patients. Data-driven technology holds promise for the prediction of occupational burnout before it occurs. Early warning signs of burnout would facilitate preemptive institutional responses for preventing individual, organizational, and public health consequences of occupational burnout. This protocol describes the design and methodology for the decentralized Burnout PRedictiOn Using Wearable aNd ArtIficial IntelligEnce (BROWNIE) Study. This study aims to develop predictive models of occupational burnout and estimate burnout-associated costs using consumer-grade wearable smartwatches and systems-level data. Methods A total of 360 registered nurses (RNs) will be recruited in 3 cohorts. These cohorts will serve as training, testing, and validation datasets for developing predictive models. Subjects will consent to one year of participation, including the daily use of a commodity smartwatch that collects heart rate, step count, and sleep data. Subjects will also complete online baseline and quarterly surveys assessing psychological, workplace, and sociodemographic factors. Routine administrative systems-level data on nursing care outcomes will be abstracted weekly. Discussion The BROWNIE study was designed to be decentralized and asynchronous to minimize any additional burden on RNs and to ensure that night shift RNs would have equal accessibility to study resources and procedures. The protocol employs novel engagement strategies with participants to maintain compliance and reduce attrition to address the historical challenges of research using wearable devices. Trial Registration NCT05481138.