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Abstract Purpose Synovial fibrosis (SFb) formation and turnover attributable to knee osteoarthritis (KOA) can impart painful stiffness and persist following arthroplasty. To supplement joint conditioning aimed at maximizing peri‐operative function, we evaluated the antifibrotic effect of Minoxidil (MXD) on formation of pyridinoline (Pyd) cross‐links catalyzed by Plod2‐encoded lysyl hydroxylase (LH)2b that strengthen newly synthesized type‐I collagen (COL1) in fibroblastic synovial cells (FSCs) from KOA patients. MXD was predicted to decrease Pyd without significant alterations to Col1a1 transcription by FSCs stimulated with transforming growth factor (TGF)β1. Methods Synovium from 10 KOA patients grouped by SFb severity was preserved for picrosirius and LH2b histology or culture. Protein and RNA were purified from fibrotic FSCs after 8 days with or without 0.5 µM MXD and/or 4 ng/mL of TGFβ1. COL1 and Pyd protein concentrations from ELISA and expression of Col1a1, Acta2, and Plod2 genes by qPCR were compared by parametric tests with α = 0.05. Results Histological LH2b expression corresponded to SFb severity. MXD attenuated COL1 output in KOA FSCs but only in the absence of TGFβ1 and consistently decreased Pyd under all conditions with significant downregulation of Plod2 but minimal alterations to Col1a1 and Acta2 transcripts. Conclusions MXD is an attractive candidate for local antifibrotic pharmacotherapy for SFb by compromising the integrity of newly formed fibrous deposits by FSCs during KOA and following arthroplasty. Targeted antifibrotic supplementation could improve physical therapy and arthroscopic lysis strategies aimed at breaking down joint scarring. However, the effect of MXD on other joint‐specific TGFβ1‐mediated processes or non‐fibrotic components requires further investigation.

Objective: Early identification of individuals who are at risk for suicide is crucial in supporting suicide prevention. Machine learning is emerging as a promising approach to support this objective. Machine learning is broadly defined as a set of mathematical models and computational algorithms designed to automatically learn complex patterns between predictors and outcomes from example data, without being explicitly programmed to do so. The model's performance continuously improves over time by learning from newly available data.Method: This concept paper explores how machine learning approaches applied to healthcare data obtained from electronic health records, including billing and claims data, can advance our ability to accurately predict future suicidal behavior.Results: We provide a general overview of machine learning concepts, summarize exemplar studies, describe continued challenges, and propose innovative research directions.Conclusion: Machine learning has potential for improving estimation of suicide risk, yet important challenges and opportunities remain. Further research can focus on incorporating evolving methods for addressing data imbalances, understanding factors that affect generalizability across samples and healthcare systems, expanding the richness of the data, leveraging newer machine learning approaches, and developing automatic learning systems.

The Soviet military mapping project was the most comprehensive cartographic endeavour of the twentieth century. The resulting maps have been commercially available to the West since at least 1993, when a Latvian business first offered Soviet plans of Western cities for sale at the 16th International Cartographic Conference in Cologne, Germany. Covering the globe at a range of scales, Soviet military maps provide a fascinating – if disconcerting – view of familiar territory with a striking aesthetic. But they also provide a substantial untapped geospatial resource, often with an unparalleled level of topographic detail. This paper gives an overview of the Soviet global military mapping programme and its coverage of Poland, including the 1:25,000-scale city plan of Warsaw (printed in 1981). By illustrating the extensive topographic symbology employed at various scales of mapping, it suggests how these maps may offer scope for regional studies and how their cartographic language can provide some solutions for addressing the ongoing challenges of mapping the globe.

Biosensors enable observation and understanding of latent physiological occurrences otherwise unknown or invasively detected. Wearable biosensors monitoring physiological constructs across a wide variety of mental and physical health conditions have become an important trend in innovative research methodologies. Within substance use research, explorations of biosensor technology commonly focus on identifying physiological indicators of intoxication to increase understanding of addiction etiology and to inform treatment recommendations. In this review, we examine the state of research in this area as it pertains to treatment of alcohol use disorders specifically highlighting the gaps in our current knowledge with recommendations for future research. Annually, alcohol use disorders affect approximately 15 million individuals. A primary focus of existing wearable technology-based research among people with alcohol use disorders is identifying alcohol intoxication. A large benefit of wearable biosensors for this purpose is they provide continuous readings in a passive manner compared with the gold standard measure of blood alcohol content (BAC) traditionally measured intermittently by breathalyzer or blood draw. There are two primary means of measuring intoxication with biosensors: gait and sweat. Gait changes have been measured via smart sensors placed on the wrist, in the shoe, and mobile device sensors in smart phones. Sweat measured by transdermal biosensors detects the presence of alcohol in the blood stream correlating to BAC. Transdermal biosensors have been designed in tattoos/skin patches, shirts, and most commonly, devices worn on the ankle or wrist. Transdermal devices were initially developed to help monitor court-ordered sobriety among offenders with alcohol use disorder. These devices now prove most useful in continuously tracking consumption throughout clinical trials for behavioral treatment modalities. More recent research has started exploring the uses for physical activity trackers and physiological arousal sensors to guide behavioral interventions for relapse prevention. While research has begun to demonstrate wearable devices' utility in reducing alcohol consumption among individuals aiming to cutdown on their drinking, monitoring sustained abstinence in studies exploring contingency management for alcohol use disorders, and facilitating engagement in activity-based treatment interventions, their full potential to further aid in understanding of, and treatment for, alcohol use disorders has yet to be explored.