Lifting Hofmeister's Curse: Impact of Cations on Diffusion, Hydrogen Bonding, and Clustering of Water.

Water plays a role in the stability, reactivity, and dynamics of the solutes that it contains. The presence of ions alters this capacity by changing the dynamics and structure of water. However, our understanding of how and to what extent this occurs is still incomplete. Here, a study of the low-frequency Raman spectra of aqueous solutions of various cations by using optical Kerr-effect spectroscopy is presented. This technique allows for the measurement of the changes that ions cause in both the diffusive dynamics and the vibrations of the hydrogen-bond structure of water. It is found that when salts are added, some of the water molecules become part of the ion solvation layers, while the rest retain the same diffusional properties as those of pure water. The slowing of the dynamics of the water molecules in the solvation shell of each ion was found to depend on its charge density at infinite dilution conditions and on its position in the Hofmeister series at higher concentrations. It is also observed that all cations weaken the hydrogen-bond structure of the solution and that this weakening depends only on the size of the cation. Finally, evidence is found that ions tend to form amorphous aggregates, even at very dilute concentrations. This work provides a novel approach to water dynamics that can be used to better study the mechanisms of solute nucleation and crystallization, the structural stability of biomolecules, and the dynamic properties of complex solutions, such as water-in-salt electrolytes.

Rapid assessment of the blood-feeding histories of wild-caught malaria mosquitoes using mid-infrared spectroscopy and machine learning.

BACKGROUND: The degree to which Anopheles mosquitoes prefer biting humans over other vertebrate hosts, i.e. the human blood index (HBI), is a crucial parameter for assessing malaria transmission risk. However, existing techniques for identifying mosquito blood meals are demanding in terms of time and effort, involve costly reagents, and are prone to inaccuracies due to factors such as cross-reactivity with other antigens or partially digested blood meals in the mosquito gut. This study demonstrates the first field application of mid-infrared spectroscopy and machine learning (MIRS-ML), to rapidly assess the blood-feeding histories of malaria vectors, with direct comparison to PCR assays. METHODS AND RESULTS: Female Anopheles funestus mosquitoes (N = 1854) were collected from rural Tanzania and desiccated then scanned with an attenuated total reflectance Fourier-transform Infrared (ATR-FTIR) spectrometer. Blood meals were confirmed by PCR, establishing the 'ground truth' for machine learning algorithms. Logistic regression and multi-layer perceptron classifiers were employed to identify blood meal sources, achieving accuracies of 88%-90%, respectively, as well as HBI estimates aligning well with the PCR-based standard HBI. CONCLUSIONS: This research provides evidence of MIRS-ML effectiveness in classifying blood meals in wild Anopheles funestus, as a potential complementary surveillance tool in settings where conventional molecular techniques are impractical. The cost-effectiveness, simplicity, and scalability of MIRS-ML, along with its generalizability, outweigh minor gaps in HBI estimation. Since this approach has already been demonstrated for measuring other entomological and parasitological indicators of malaria, the validation in this study broadens its range of use cases, positioning it as an integrated system for estimating pathogen transmission risk and evaluating the impact of interventions.

Screening of malaria infections in human blood samples with varying parasite densities and anaemic conditions using AI-Powered mid-infrared spectroscopy.

BACKGROUND: Effective testing for malaria, including the detection of infections at very low densities, is vital for the successful elimination of the disease. Unfortunately, existing methods are either inexpensive but poorly sensitive or sensitive but costly. Recent studies have shown that mid-infrared spectroscopy coupled with machine learning (MIRs-ML) has potential for rapidly detecting malaria infections but requires further evaluation on diverse samples representative of natural infections in endemic areas. The aim of this study was, therefore, to demonstrate a simple AI-powered, reagent-free, and user-friendly approach that uses mid-infrared spectra from dried blood spots to accurately detect malaria infections across varying parasite densities and anaemic conditions. METHODS: Plasmodium falciparum strains NF54 and FCR3 were cultured and mixed with blood from 70 malaria-free individuals to create various malaria parasitaemia and anaemic conditions. Blood dilutions produced three haematocrit ratios (50%, 25%, 12.5%) and five parasitaemia levels (6%, 0.1%, 0.002%, 0.00003%, 0%). Dried blood spots were prepared on Whatman™ filter papers and scanned using attenuated total reflection-Fourier Transform Infrared (ATR-FTIR) for machine-learning analysis. Three classifiers were trained on an 80%/20% split of 4655 spectra: (I) high contrast (6% parasitaemia vs. negative), (II) low contrast (0.00003% vs. negative) and (III) all concentrations (all positive levels vs. negative). The classifiers were validated with unseen datasets to detect malaria at various parasitaemia levels and anaemic conditions. Additionally, these classifiers were tested on samples from a population survey in malaria-endemic villages of southeastern Tanzania. RESULTS: The AI classifiers attained over 90% accuracy in detecting malaria infections as low as one parasite per microlitre of blood, a sensitivity unattainable by conventional RDTs and microscopy. These laboratory-developed classifiers seamlessly transitioned to field applicability, achieving over 80% accuracy in predicting natural P. falciparum infections in blood samples collected during the field survey. Crucially, the performance remained unaffected by various levels of anaemia, a common complication in malaria patients. CONCLUSION: These findings suggest that the AI-driven mid-infrared spectroscopy approach holds promise as a simplified, sensitive and cost-effective method for malaria screening, consistently performing well despite variations in parasite densities and anaemic conditions. The technique simply involves scanning dried blood spots with a desktop mid-infrared scanner and analysing the spectra using pre-trained AI classifiers, making it readily adaptable to field conditions in low-resource settings. In this study, the approach was successfully adapted to field use, effectively predicting natural malaria infections in blood samples from a population-level survey in Tanzania. With additional field trials and validation, this technique could significantly enhance malaria surveillance and contribute to accelerating malaria elimination efforts.

Frontostriatothalamic effective connectivity and dopaminergic function in the psychosis continuum.

Dysfunction of fronto-striato-thalamic (FST) circuits is thought to contribute to dopaminergic dysfunction and symptom onset in psychosis, but it remains unclear whether this dysfunction is driven by aberrant bottom-up subcortical signalling or impaired top-down cortical regulation. We used spectral dynamic causal modelling of resting-state functional MRI to characterize the effective connectivity of dorsal and ventral FST circuits in a sample of 46 antipsychotic-naïve first-episode psychosis patients and 23 controls and an independent sample of 36 patients with established schizophrenia and 100 controls. We also investigated the association between FST effective connectivity and striatal 18F-DOPA uptake in an independent healthy cohort of 33 individuals who underwent concurrent functional MRI and PET. Using a posterior probability threshold of 0.95, we found that midbrain and thalamic connectivity were implicated as dysfunctional across both patient groups. Dysconnectivity in first-episode psychosis patients was mainly restricted to the subcortex, with positive symptom severity being associated with midbrain connectivity. Dysconnectivity between the cortex and subcortical systems was only apparent in established schizophrenia patients. In the healthy 18F-DOPA cohort, we found that striatal dopamine synthesis capacity was associated with the effective connectivity of nigrostriatal and striatothalamic pathways, implicating similar circuits to those associated with psychotic symptom severity in patients. Overall, our findings indicate that subcortical dysconnectivity is evident in the early stages of psychosis, that cortical dysfunction may emerge later in the illness, and that nigrostriatal and striatothalamic signalling are closely related to striatal dopamine synthesis capacity, which is a robust marker for psychosis.

Development and validation of a predictive model for choledocholithiasis.

BACKGROUND: Gallstone-related conditions affect a significant portion of the population, with varying prevalence among different ethnic groups. Complications such as pancreatitis and cholangitis are associated with the presence of common bile duct (CBD) stones. Existing guidelines for diagnosing choledocholithiasis lack precision, leading to excessive use of invasive procedures like endoscopic retrograde cholangiopancreatography (ERCP). METHODS: A prospective study was conducted at Hospital Central "Dr. Ignacio Morones Prieto," involving 374 patients in the development cohort and 154 patients in the validation cohort. Patients meeting inclusion criteria underwent biochemical testing and ultrasonography. A predictive scoring system was developed using logistic regression and validated in an independent cohort. Clinical and laboratory variables were collected, and model performance was assessed using receiver-operator characteristic (ROC) curves. RESULTS: The predictive model incorporated variables such as age, pancreatitis, cholangitis, bilirubin levels, and CBD stone presence on ultrasound. The model demonstrated an area under the ROC curve (AUC) of 93.81% in the validation dataset. By adjusting the threshold defining high-risk probability to 40%, the model improved specificity and sensitivity compared to existing guidelines. Notably, the model reclassified patients, leading to a more accurate risk assessment. CONCLUSIONS: The developed algorithm accurately predicts choledocholithiasis non-invasively in patients with symptomatic gallstones. This tool has the potential to reduce reliance on costly or invasive procedures like magnetic resonance cholangiopancreatography and ERCP, offering a more efficient and cost-effective approach to patient management. The user-friendly calculator developed in this study could streamline diagnostic procedures, particularly in resource-limited healthcare settings, ultimately improving patient care.

SEMA3: A free smartphone platform for daily life surveys.

Traditionally, behavioral, social, and health science researchers have relied on global/retrospective survey methods administered cross-sectionally (i.e., on a single occasion) or longitudinally (i.e., on several occasions separated by weeks, months, or years). More recently, social and health scientists have added daily life survey methods (also known as intensive longitudinal methods or ambulatory assessment) to their toolkit. These methods (e.g., daily diaries, experience sampling, ecological momentary assessment) involve dense repeated assessments in everyday settings. To facilitate research using daily life survey methods, we present SEMA3 ( http://www.SEMA3.com ), a platform for designing and administering intensive longitudinal daily life surveys via Android and iOS smartphones. SEMA3 fills an important gap by providing researchers with a free, intuitive, and flexible platform with basic and advanced functionality. In this article, we describe SEMA3's development history and system architecture, provide an overview of how to design a study using SEMA3 and outline its key features, and discuss the platform's limitations and propose directions for future development of SEMA3.

Using transfer learning and dimensionality reduction techniques to improve generalisability of machine-learning predictions of mosquito ages from mid-infrared spectra.

BACKGROUND: Old mosquitoes are more likely to transmit malaria than young ones. Therefore, accurate prediction of mosquito population age can drastically improve the evaluation of mosquito-targeted interventions. However, standard methods for age-grading mosquitoes are laborious and costly. We have shown that Mid-infrared spectroscopy (MIRS) can be used to detect age-specific patterns in mosquito cuticles and thus can be used to train age-grading machine learning models. However, these models tend to transfer poorly across populations. Here, we investigate whether applying dimensionality reduction and transfer learning to MIRS data can improve the transferability of MIRS-based predictions for mosquito ages. METHODS: We reared adults of the malaria vector Anopheles arabiensis in two insectaries. The heads and thoraces of female mosquitoes were scanned using an attenuated total reflection-Fourier transform infrared spectrometer, which were grouped into two different age classes. The dimensionality of the spectra data was reduced using unsupervised principal component analysis or t-distributed stochastic neighbour embedding, and then used to train deep learning and standard machine learning classifiers. Transfer learning was also evaluated to improve transferability of the models when predicting mosquito age classes from new populations. RESULTS: Model accuracies for predicting the age of mosquitoes from the same population as the training samples reached 99% for deep learning and 92% for standard machine learning. However, these models did not generalise to a different population, achieving only 46% and 48% accuracy for deep learning and standard machine learning, respectively. Dimensionality reduction did not improve model generalizability but reduced computational time. Transfer learning by updating pre-trained models with 2% of mosquitoes from the alternate population improved performance to ~ 98% accuracy for predicting mosquito age classes in the alternative population. CONCLUSION: Combining dimensionality reduction and transfer learning can reduce computational costs and improve the transferability of both deep learning and standard machine learning models for predicting the age of mosquitoes. Future studies should investigate the optimal quantities and diversity of training data necessary for transfer learning and the implications for broader generalisability to unseen datasets.

A Second Glass Transition Observed in Single-Component Homogeneous Liquids Due to Intramolecular Vitrification.

On supercooling a liquid, the viscosity rises rapidly until at the glass transition it vitrifies into an amorphous solid accompanied by a steep drop in the heat capacity. Therefore, a pure homogeneous liquid is not expected to display more than one glass transition. Here we show that a family of single-component homogeneous molecular liquids, titanium tetraalkoxides, exhibit two calorimetric glass transitions of comparable magnitude, one of which is the conventional glass transition associated with dynamic arrest of the bulk liquid properties, while the other is associated with the freezing out of intramolecular degrees of freedom. Such intramolecular vitrification is likely to be found in molecules in which low-frequency terahertz intramolecular motion is coupled to the surrounding liquid. These results imply that intramolecular barrier-crossing processes, typically associated with chemical reactivity, do not necessarily follow the Arrhenius law but may freeze out at a finite temperature.

The effect of psychological treatment on repetitive negative thinking in youth depression and anxiety: a meta-analysis and meta-regression.

BACKGROUND: Depression and anxiety are prevalent in youth populations and typically emerge during adolescence. Repetitive negative thinking (RNT) is a putative transdiagnostic mechanism with consistent associations with depression and anxiety. Targeting transdiagnostic processes like RNT for youth depression and anxiety may offer more targeted, personalised and effective treatment. METHODS: A meta-analysis was conducted to examine the effect of psychological treatments on RNT, depression and anxiety symptoms in young people with depression or anxiety, and a meta-regression to examine relationships between outcomes. RESULTS: Twenty-eight randomised controlled trials examining 17 different psychological interventions were included. Effect sizes were small to moderate across all outcomes (Hedge's g depression = -0.47, CI -0.77 to -0.17; anxiety = -0.42, CI -0.65 to -0.20; RNT = -0.45, CI -0.67 to -0.23). RNT-focused and non-RNT focused approaches had comparable effects; however, those focusing on modifying the process of RNT had significantly larger effects on RNT than those focusing on modifying negative thought content. Meta-regression revealed a significant relationship between RNT and depression outcomes only across all intervention types and with both depression and anxiety for RNT focused interventions only. CONCLUSION: Consistent with findings in adults, this review provides evidence that reducing RNT with psychological treatment is associated with improvements in depression and anxiety in youth. Targeting RNT specifically may not lead to better outcomes compared to general approaches; however, focusing on modifying the process of RNT may be more effective than targeting content. Further research is needed to determine causal pathways.

Key considerations, target product profiles, and research gaps in the application of infrared spectroscopy and artificial intelligence for malaria surveillance and diagnosis.

Studies on the applications of infrared (IR) spectroscopy and machine learning (ML) in public health have increased greatly in recent years. These technologies show enormous potential for measuring key parameters of malaria, a disease that still causes about 250 million cases and 620,000 deaths, annually. Multiple studies have demonstrated that the combination of IR spectroscopy and machine learning (ML) can yield accurate predictions of epidemiologically relevant parameters of malaria in both laboratory and field surveys. Proven applications now include determining the age, species, and blood-feeding histories of mosquito vectors as well as detecting malaria parasite infections in both humans and mosquitoes. As the World Health Organization encourages malaria-endemic countries to improve their surveillance-response strategies, it is crucial to consider whether IR and ML techniques are likely to meet the relevant feasibility and cost-effectiveness requirements-and how best they can be deployed. This paper reviews current applications of IR spectroscopy and ML approaches for investigating malaria indicators in both field surveys and laboratory settings, and identifies key research gaps relevant to these applications. Additionally, the article suggests initial target product profiles (TPPs) that should be considered when developing or testing these technologies for use in low-income settings.

A Personalized, Transdiagnostic Smartphone Intervention (Mello) Targeting Repetitive Negative Thinking in Young People With Depression and Anxiety: Pilot Randomized Controlled Trial.

BACKGROUND: Repetitive negative thinking (RNT) is a key transdiagnostic mechanism underpinning depression and anxiety. Using "just-in-time adaptive interventions" via smartphones may disrupt RNT in real time, providing targeted and personalized intervention. OBJECTIVE: This pilot randomized controlled trial evaluates the feasibility, acceptability, and preliminary clinical outcomes and mechanisms of Mello-a fully automated, personalized, transdiagnostic, and mechanistic smartphone intervention targeting RNT in young people with depression and anxiety. METHODS: Participants with heightened depression, anxiety, and RNT were recruited via social media and randomized to receive Mello or a nonactive control over a 6-week intervention period. Assessments were completed via Zoom sessions at baseline and at 3 and 6 weeks after baseline. RESULTS: The findings supported feasibility and acceptability, with high rates of recruitment (N=55), uptake (55/64, 86% of eligible participants), and retention (52/55, 95% at 6 weeks). Engagement was high, with 90% (26/29) and 59% (17/29) of the participants in the Mello condition still using the app during the third and sixth weeks, respectively. Greater reductions in depression (Cohen d=0.50), anxiety (Cohen d=0.61), and RNT (Cohen d=0.87) were observed for Mello users versus controls. Mediation analyses suggested that changes in depression and anxiety were accounted for by changes in RNT. CONCLUSIONS: The results indicate that mechanistic, targeted, and real-time technology-based solutions may provide scalable and effective interventions that advance the treatment of youth mental ill health. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12621001701819; http://tinyurl.com/4d3jfj9f.

The role of insight, social rank, mindfulness and self-compassion in depression following first episode psychosis.

Gaining awareness of psychosis (i.e., insight) is linked to depression, particularly in the post-acute phase of psychosis. Informed by social rank theory, we examined whether the insight-depression relationship is explained by reduced social rank related to psychosis and whether self-compassion (including uncompassionate self-responding [UCS] and compassionate self-responding [CSR]) and mindfulness buffered the relationship between social rank and depression in individuals with first episode psychosis during the post-acute phase. Participants were 145 young people (Mage  = 20.81; female = 66) with first episode psychosis approaching discharge from an early psychosis intervention centre. Questionnaires and interviews assessed insight, depressive symptoms, perceived social rank, self-compassion, mindfulness and illness severity. Results showed that insight was not significantly associated to depression and thus no mediation analysis was conducted. However, lower perceived social rank was related to higher depression, and this relationship was moderated by self-compassion and, more specifically, UCS. Mindfulness was related to depression but had no moderating effect on social rank and depression. Results supported previous findings that depressive symptoms are common during the post-acute phase. The role of insight in depression for this sample is unclear and may be less important during the post-acute phase than previously considered. Supporting social rank theory, the results suggest that low perceived social rank contributes to depression, and reducing UCS may ameliorate this effect. UCS, social rank and possibly mindfulness may be valuable intervention targets for depression intervention and prevention efforts in the recovery of psychosis.

Integrating digital interventions with clinical practice in youth mental health services.

OBJECTIVE: Integrating digital technologies with clinical practice promises to improve access and enhance care in the context of high service demand and constrained capacity. METHOD: We outline the emerging research in the integration of digital tools in clinical care, known as blended care, and provide case examples of mental health technology platforms currently in use, summarise findings regarding novel technologies such as virtual reality, and outline real-world implementation challenges and potential solutions. RESULTS: Recent evidence shows that blended care approaches are clinically effective and improve service efficiency. Youth-specific technologies such as moderated online social therapy (MOST) are achieving a range of positive clinical and functional outcomes, while emerging technologies like virtual reality have strong evidence in anxiety disorder, and accumulating evidence in psychotic conditions. Implementation science frameworks show promise in helping overcome the common challenges faced in real-world adoption and ongoing use. CONCLUSION: The integrated, blended use of digital mental health technologies with face-to-face clinical care has the potential to improve care quality for young people while helping overcome the growing challenges faced by youth mental health service providers.

The Australian Early Psychosis Collaborative Consortium (AEPCC): Improving Clinical Care in Early Psychosis.

OBJECTIVES: The field of early psychosis has undergone considerable expansion over the last few decades and has a strong evidence base of effectiveness. Like all areas of healthcare, however, early psychosis services need to more consistently deliver higher quality care to achieve better outcomes for patients and families. A national clinical research infrastructure is urgently required to enable the sector to deliver the highest quality care and expand and translate evidence more quickly and efficiently. This paper describes the establishment of the Australian Early Psychosis Collaborative Consortium (AEPCC) that aims to achieve this. CONCLUSION: AEPCC is the first of its kind in Australia (and internationally). It will deliver the required clinical research infrastructure through the implementation of a clinical quality registry, clinical trials and translation network, and lived experience network. AEPCC will provide a critical resource to better understand the state of early psychosis care, and trial new interventions on a scale that has not previously been possible in Australia.

A Randomized Controlled Trial on the Effect of Local Insulin Glargine on Venous Ulcer Healing.

INTRODUCTION: To determine whether the local administration of insulin glargine compared with placebo in nondiabetic patients with venous ulcers (VUs) leads to increased wound healing. METHODS: A randomized controlled trial using a split-plot design was performed in 36 adults with leg VUs >25 cm2 and more than 3 mo of evolution. Each hemi-wound received either 10 UI insulin glargine or saline solution once a day for 7 d. Size of the wounds, thermal asymmetry, the number of blood vessels, and the percentage area of collagen content in wound biopsies were assessed at baseline and after 7 d of treatment. Blood capillary glucose was monitored once a day after the insulin injection. RESULTS: After 7 d of treatment, the hemi-wounds treated with insulin glargine were significantly smaller, had less thermal asymmetry, more blood vessels, and more collagen content than the saline-treated side. Correlation between thermal asymmetry and the number of blood vessels was also found (r2 = 66.2, P < 0.001). No patient presented capillary glucose levels ≤3.3 mmol/L nor any adverse effects. CONCLUSIONS: In nondiabetic patients with chronic VUs, the topical administration of insulin glargine seems to be safe and promotes wound healing and tissue repair after 7 d of treatment.

Research to Clinical Practice-Youth seeking mental health information online and its impact on the first steps in the patient journey.

BACKGROUND: Online searches about anxiety and depression are recorded every 3-5 s. As such, information and communication technologies (ICT) have enormous potential to enable or impair help-seeking and patient-professional interactions. Youth studies indicate that ICT searches are undertaken before initial mental health consultations, but no publications have considered how this online activity affects the first steps of the patient journey in youth mental health settings. METHODS: State-of-the-art review using an iterative, evidence mapping approach to identify key literature and expert consensus to synthesize and prioritise clinical and research issues. RESULTS: Adolescents and young adults are more likely to seek health advice via online search engines or social media platforms than from a health professional. Young people not only search user-generated content and social media to obtain advice and support from online communities but increasingly contribute personal information online. CONCLUSIONS: A major clinical challenge is to raise professional awareness of the likely impact of this activity on mental health consultations. Potential strategies range from modifying the structure of clinical consultations to ensure young people are able to disclose ICT activities related to mental health, through to the development and implementation of 'internet prescriptions' and a youth-focused 'toolkit'.

A comparison of experiences of care and expressed emotion among caregivers of young people with first-episode psychosis or borderline personality disorder features.

OBJECTIVE: Caregivers of individuals with severe mental illness often experience significant negative experiences of care, which can be associated with higher levels of expressed emotion. Expressed emotion is potentially a modifiable target early in the course of illness, which might improve outcomes for caregivers and patients. However, expressed emotion and caregiver experiences in the early stages of disorders might be moderated by the type of severe mental illness. The aim was to determine whether experiences of the caregiver role and expressed emotion differ in caregivers of young people with first-episode psychosis versus young people with 'first-presentation' borderline personality disorder features. METHOD: Secondary analysis of baseline (pre-treatment) data from three clinical trials focused on improving caregiver outcomes for young people with first-episode psychosis and young people with borderline personality disorder features was conducted (ACTRN12616000968471, ACTRN12616000304437, ACTRN12618000616279). Caregivers completed self-report measures of experiences of the caregiver role and expressed emotion. Multivariate generalised linear models and moderation analyses were used to determine group differences. RESULTS: Data were available for 265 caregivers. Higher levels of negative experiences and expressed emotion, and stronger correlations between negative experiences and expressed emotion domains, were found in caregivers of young people with borderline personality disorder than first-episode psychosis. Caregiver group (borderline personality disorder, first-episode psychosis) moderated the relationship between expressed emotion and caregiver experiences in the domains of need to provide backup and positive personal experiences. CONCLUSION: Caregivers of young people with borderline personality disorder experience higher levels of negative experiences related to their role and expressed emotion compared with caregivers of young people with first-episode psychosis. The mechanisms underpinning associations between caregiver experiences and expressed emotion differ between these two caregiver groups, indicating that different supports are needed. For borderline personality disorder caregivers, emotional over-involvement is associated with both negative and positive experiences, so a more detailed understanding of the nature of emotional over-involvement for each relationship is required to guide action.

Digital Interventions for Psychological Well-being in University Students: Systematic Review and Meta-analysis.

BACKGROUND: Life at university provides important opportunities for personal growth; however, this developmental phase also coincides with the peak period of risk for the onset of mental health disorders. In addition, specific university lifestyle factors, including impaired sleep and academic and financial stress, are known to exacerbate psychological distress in students. As a result, university students have been identified as a vulnerable population who often experience significant barriers to accessing psychological treatment. Digital psychological interventions are emerging as a promising solution for this population, but their effectiveness remains unclear. OBJECTIVE: This systematic review and meta-analysis aimed to assess digital interventions targeting psychological well-being among university students. METHODS: Database searches were conducted on December 2, 2021, via Embase, MEDLINE, PsycINFO, and Web of Science. RESULTS: A total of 13 eligible studies were identified, 10 (77%) of which were included in the meta-analysis. Mean pre-post effect sizes indicated that such interventions led to small and significant improvement in psychological well-being (Hedges g=0.32, 95% CI 0.23-0.4; P<.001). These effects remained, albeit smaller, when studies that included a wait-list control group were excluded (Hedges g=0.22, 95% CI 0.08-0.35; P=.002). An analysis of acceptance and commitment therapy approaches revealed small and significant effects (k=6; Hedges g=0.35, 95% CI 0.25-0.45; P<.001). CONCLUSIONS: Digital psychological interventions hold considerable promise for university students, although features that optimize service delivery and outcomes require further assessment. TRIAL REGISTRATION: PROSPERO CRD42020196654; https:/www.crd.york.ac.uk/prospero/display_record.php?RecordID=196654.

Predicting Perceived Exhaustion in Rehabilitation Exercises Using Facial Action Units.

Physical exercise has become an essential tool for treating various non-communicable diseases (also known as chronic diseases). Due to this, physical exercise allows to counter different symptoms and reduce some risk of death factors without medication. A solution to support people in doing exercises is to use artificial systems that monitor their exercise progress. While one crucial aspect is to monitor the correct physical motions for rehabilitative exercise, another essential element is to give encouraging feedback during workouts. A coaching system can track a user's exhaustion and give motivating feedback accordingly to boost exercise adherence. For this purpose, this research investigates whether it is possible to predict the subjective exhaustion level based on non-invasive and non-wearable technology. A novel data set was recorded with the facial record as the primary predictor and individual exhaustion levels as the predicted variable. 60 participants (30 male, 30 female) took part in the data recording. 17 facial action units (AU) were extracted as predictor variables for the perceived subjective exhaustion measured using the BORG scale. Using the predictor and the target variables, several regression and classification methods were evaluated aiming to predict exhaustion. The results showed that the decision tree and support vector methods provide reasonable prediction results. The limitation of the results, depending on participants being in the training data set and subjective variables (e.g., participants smiling during the exercises) were further discussed.

Diagnostic accuracy of infrared thermal imaging for detecting COVID-19 infection in minimally symptomatic patients.

INTRODUCTION: Despite being widely used as a screening tool, a rigorous scientific evaluation of infrared thermography for the diagnosis of minimally symptomatic patients suspected of having COVID-19 infection has not been performed. METHODS: A consecutive sample of 60 adult individuals with a history of close contact with COVID-19 infected individuals and mild respiratory symptoms for less than 7 days and 20 confirmed COVID-19 negative healthy volunteers were enrolled in the study. Infrared thermograms of the face were obtained with a mobile camera, and RT-PCR was used as the reference standard test to diagnose COVID-19 infection. Temperature values and distribution of the face of healthy volunteers and patients with and without COVID-19 infection were then compared. RESULTS: Thirty-four patients had an RT-PCR confirmed diagnosis of COVID-19 and 26 had negative test results. The temperature asymmetry between the lacrimal caruncles and the forehead was significantly higher in COVID-19 positive individuals. Through a random forest analysis, a cut-off value of 0.55°C was found to discriminate with an 82% accuracy between patients with and without COVID-19 confirmed infection. CONCLUSIONS: Among adults with a history of COVID-19 exposure and mild respiratory symptoms, a temperature asymmetry of ≥ 0.55°C between the lacrimal caruncle and the forehead is highly suggestive of COVID-19 infection. This finding questions the widespread use of the measurement of absolute temperature values of the forehead as a COVID-19 screening tool.