Moderation Analysis of a couple-based HIV/STI Intervention Among Heterosexual Couples in the Criminal Legal System Experiencing Intimate Partner Violence: Results from a Randomized Controlled Trial.

Since the COVID-19 pandemic, intimate partner violence (IPV) rates have increased in the United States. Although accumulating research has documented the effectiveness of couple-based interventions in reducing HIV/STIs, it remains unclear whether they are effective and safe for couples experiencing IPV. We used moderation analysis from a randomized clinical trial to evaluate whether a couples-based HIV/STI intervention may have differential effectiveness in reducing HIV/STI risks among couples where one or both partners reported experiencing IPV compared to couples without such IPV among a sample of 230 men at risk for HIV/STIs who reported using drugs and were mandated to community supervision settings in New York City and their main female sexual partners. The findings of this study suggest that the effectiveness of this evidence-based couple HIV intervention in reducing condomless sex and other HIV/STI risks did not differ between couples with IPV compared to couples without IPV. Intimate partners who use drugs and are involved in the criminal legal system are disproportionately impacted by both HIV/STIs and IPV, underscoring the importance of couple-level interventions that may be scaled up to address the dyadic HIV risks and IPV together in community supervision settings.

Wide-Bandwidth Nanocomposite-Sensor Integrated Smart Mask for Tracking Multiphase Respiratory Activities.

Wearing masks has been a recommended protective measure due to the risks of coronavirus disease 2019 (COVID-19) even in its coming endemic phase. Therefore, deploying a "smart mask" to monitor human physiological signals is highly beneficial for personal and public health. This work presents a smart mask integrating an ultrathin nanocomposite sponge structure-based soundwave sensor (≈400 µm), which allows the high sensitivity in a wide-bandwidth dynamic pressure range, i.e., capable of detecting various respiratory sounds of breathing, speaking, and coughing. Thirty-one subjects test the smart mask in recording their respiratory activities. Machine/deep learning methods, i.e., support vector machine and convolutional neural networks, are used to recognize these activities, which show average macro-recalls of ≈95% in both individual and generalized models. With rich high-frequency (≈4000 Hz) information recorded, the two-/tri-phase coughs can be mapped while speaking words can be identified, demonstrating that the smart mask can be applicable as a daily wearable Internet of Things (IoT) device for respiratory disease identification, voice interaction tool, etc. in the future. This work bridges the technological gap between ultra-lightweight but high-frequency response sensor material fabrication, signal transduction and processing, and machining/deep learning to demonstrate a wearable device for potential applications in continual health monitoring in daily life.

The Effect of Fear of the COVID-19 on Depression Among Chinese Outbound Students Studying Online in China Amid the COVID-19 Pandemic Period: The Role of Resilience and Social Support.

Objective: The present study focused on examining fear of the coronavirus disease 2019 (COVID-19) is correlated with depression and explored the potential role of resilience and social support on the association between fear of the COVID-19 (FoC) and depression among Chinese outbound students studying online in China amid the COVID-19 pandemic period. Methods: A total of 476 Chinese outbound students from different universities worldwide, currently studying via online mode in China, completed an online survey including measures on FoC, resilience, social support, and depression. Results: (1) Fear of the COVID-19 was positively correlated with depression and negatively correlated with resilience and social support. Both resilience and social support were negatively correlated with depression. Social support showed a positive correlation with resilience. (2) The effect of FoC on depression mainly occurred through two paths: the mediating effect of resilience and the moderating effect of resilience. However, the moderating effect of social support on the association between FoC and depression was not sustained in this study. Conclusion: This study indicated the mediating and moderating effects of resilience on the association between FoC and depression among Chinese outbound students studying online in China during the COVID-19 pandemic period. The current findings confirmed that resilience has significant implications in preventing negative mental states under the COVID-19 context among this particular group.

Deep significance clustering: a novel approach for identifying risk-stratified and predictive patient subgroups.

OBJECTIVE: Deep significance clustering (DICE) is a self-supervised learning framework. DICE identifies clinically similar and risk-stratified subgroups that neither unsupervised clustering algorithms nor supervised risk prediction algorithms alone are guaranteed to generate. MATERIALS AND METHODS: Enabled by an optimization process that enforces statistical significance between the outcome and subgroup membership, DICE jointly trains 3 components, representation learning, clustering, and outcome prediction while providing interpretability to the deep representations. DICE also allows unseen patients to be predicted into trained subgroups for population-level risk stratification. We evaluated DICE using electronic health record datasets derived from 2 urban hospitals. Outcomes and patient cohorts used include discharge disposition to home among heart failure (HF) patients and acute kidney injury among COVID-19 (Cov-AKI) patients, respectively. RESULTS: Compared to baseline approaches including principal component analysis, DICE demonstrated superior performance in the cluster purity metrics: Silhouette score (0.48 for HF, 0.51 for Cov-AKI), Calinski-Harabasz index (212 for HF, 254 for Cov-AKI), and Davies-Bouldin index (0.86 for HF, 0.66 for Cov-AKI), and prediction metric: area under the Receiver operating characteristic (ROC) curve (0.83 for HF, 0.78 for Cov-AKI). Clinical evaluation of DICE-generated subgroups revealed more meaningful distributions of member characteristics across subgroups, and higher risk ratios between subgroups. Furthermore, DICE-generated subgroup membership alone was moderately predictive of outcomes. DISCUSSION: DICE addresses a gap in current machine learning approaches where predicted risk may not lead directly to actionable clinical steps. CONCLUSION: DICE demonstrated the potential to apply in heterogeneous populations, where having the same quantitative risk does not equate with having a similar clinical profile.

Strategies for Vaccine Prioritization and Mass Dispensing.

We propose a system that helps decision makers during a pandemic find, in real time, the mass vaccination strategies that best utilize limited medical resources to achieve fast containments and population protection. Our general-purpose framework integrates into a single computational platform a multi-purpose compartmental disease propagation model, a human behavior network, a resource logistics model, and a stochastic queueing model for vaccination operations. We apply the modeling framework to the current COVID-19 pandemic and derive an optimal trigger for switching from a prioritized vaccination strategy to a non-prioritized strategy so as to minimize the overall attack rate and mortality rate. When vaccine supply is limited, such a mixed vaccination strategy is broadly effective. Our analysis suggests that delays in vaccine supply and inefficiencies in vaccination delivery can substantially impede the containment effort. Employing an optimal mixed strategy can significantly reduce the attack and mortality rates. The more infectious the virus, the earlier it helps to open the vaccine to the public. As vaccine efficacy decreases, the attack and mortality rates rapidly increase by multiples; this highlights the importance of early vaccination to reduce spreading as quickly as possible to lower the chances for further mutations to evolve and to reduce the excessive healthcare burden. To maximize the protective effect of available vaccines, of equal importance are determining the optimal mixed strategy and implementing effective on-the-ground dispensing. The optimal mixed strategy is quite robust against variations in model parameters and can be implemented readily in practice. Studies with our holistic modeling framework strongly support the urgent need for early vaccination in combating the COVID-19 pandemic. Our framework permits rapid custom modeling in practice. Additionally, it is generalizable for different types of infectious disease outbreaks, whereby a user may determine for a given type the effects of different interventions including the optimal switch trigger.

Research on the Use of YOLOv5 Object Detection Algorithm in Mask Wearing Recognition

Masks can help people to reduce inhalation of droplets and the risk of infection. Because of the COVID-19, many governments required people to wear marks to prevent virus spread. In some public places, there are tons of people going back and forth everyday so it's impossible to settle a human monitor to identify whether everyone wears a mask. This work uses a different training version from YOLOv5 to train the dataset of mask wearing, and we use K-means to find the most appropriate anchors for datasets. Finally, by using data augmentation we get a more accurate model. Compared to human work, this model can be faster and more accurate to find a target and it can save countless money and time.