Dissecting the Interrelationship between COVID-19 and Diabetes Mellitus.

COVID-19 disease, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to enormous morbidity and mortality worldwide. After gaining entry into the human host, the virus initially infects the upper and lower respiratory tract, subsequently invading multiple organs, including the pancreas. While on one hand, diabetes mellitus (DM) is a significant risk factor for severe COVID-19 infection and associated death, recent reports have shown the onset of DM in COVID-19-recovered patients. SARS-CoV-2 infiltrates the pancreatic islets and activates stress response and inflammatory signaling pathways, impairs glucose metabolism, and consequently leads to their death. Indeed, the pancreatic autopsy samples of COVID-19 patients reveal the presence of SARS-CoV-2 particles in ß-cells. The current review describes how the virus enters the host cells and activates an immunological response. Further, it takes a closer look into the interrelationship between COVID-19 and DM with the aim to provide mechanistic insights into the process by which SARS-CoV-2 infects the pancreas and mediates dysfunction and death of endocrine islets. The effects of known anti-diabetic interventions for COVID-19 management are also discussed. The application of mesenchymal stem cells (MSCs) as a future therapy for pancreatic ß-cells damage to reverse COVID-19-induced DM is also emphasized.

Digital phenotyping in depression diagnostics: Integrating psychiatric and engineering perspectives.

Depression is a serious medical condition and is a leading cause of disability worldwide. Current depression diagnostics and assessment has significant limitations due to heterogeneity of clinical presentations, lack of objective assessments, and assessments that rely on patients' perceptions, memory, and recall. Digital phenotyping (DP), especially assessments conducted using mobile health technologies, has the potential to greatly improve accuracy of depression diagnostics by generating objectively measurable endophenotypes. DP includes two primary sources of digital data generated using ecological momentary assessments (EMA), assessments conducted in real-time, in subjects' natural environment. This includes active EMA, data that require active input by the subject, and passive EMA or passive sensing, data passively and automatically collected from subjects' personal digital devices. The raw data is then analyzed using machine learning algorithms to identify behavioral patterns that correlate with patients' clinical status. Preliminary investigations have also shown that linguistic and behavioral clues from social media data and data extracted from the electronic medical records can be used to predict depression status. These other sources of data and recent advances in telepsychiatry can further enhance DP of the depressed patients. Success of DP endeavors depends on critical contributions from both psychiatric and engineering disciplines. The current review integrates important perspectives from both disciplines and discusses parameters for successful interdisciplinary collaborations. A clinically-relevant model for incorporating DP in clinical setting is presented. This model, based on investigations conducted by our group, delineates development of a depression prediction system and its integration in clinical setting to enhance depression diagnostics and inform the clinical decision making process. Benefits, challenges, and opportunities pertaining to clinical integration of DP of depression diagnostics are discussed from interdisciplinary perspectives.

A Survey of Forensic Psychiatry Fellowship Program Directors on Selecting Fellowship Applicants.

In the 2019-2020 academic year, there were 48 accredited forensic psychiatry fellowship programs in the United States. Programs vary in application requirements and timeline. There are no published objective data on factors that fellowship program directors (PDs) use when selecting fellows. We created an electronic survey that was emailed to PDs via a list from the Association of Directors of Forensic Psychiatry Fellowships. The survey was open November 6, 2019 to December 31, 2019. Twenty-five PDs participated from programs ranging in size from one to six positions, receiving zero to 30 applications. The most important factors when selecting a candidate to interview were "perceived commitment to specialty" and "perceived interest in your program." The most important factors when offering a position were "interpersonal skills" and "interactions during interview." The least important factors in both categories were USMLE/COMLEX scores and honor society membership(s). "Lack of a set timeline" during the application process was the most frequently cited difficulty (via multiple choice) during the application and interview process. Our study is the first to provide quantitative data regarding factors that forensic psychiatry fellowship PDs use to evaluate applicants in decisions regarding offering interviews and positions.

Fusing Location Data for Depression Prediction.

Recent studies have demonstrated that geographic location features collected using smartphones can be a powerful predictor for depression. While location information can be conveniently gathered by GPS, typical datasets suffer from significant periods of missing data due to various factors (e.g., phone power dynamics, limitations of GPS). A common approach is to remove the time periods with significant missing data before data analysis. In this paper, we develop an approach that fuses location data collected from two sources: GPS and WiFi association records, on smartphones, and evaluate its performance using a dataset collected from 79 college students. Our evaluation demonstrates that our data fusion approach leads to significantly more complete data. In addition, the features extracted from the more complete data present stronger correlation with self-report depression scores, and lead to depression prediction with much higher F 1 scores (up to 0.76 compared to 0.5 before data fusion). We further investigate the scenerio when including an additional data source, i.e., the data collected from a WiFi network infrastructure. Our results show that, while the additional data source leads to even more complete data, the resultant F 1 scores are similar to those when only using the location data (i.e., GPS and WiFi association records) from the phones.

Automatic Depression Prediction Using Internet Traffic Characteristics on Smartphones.

Depression is a serious mental health problem. Recently, researchers have proposed novel approaches that use sensing data collected passively on smartphones for automatic depression screening. While these studies have explored several types of sensing data (e.g., location, activity, conversation), none of them has leveraged Internet traffic of smartphones, which can be collected with little energy consumption and the data is insensitive to phone hardware. In this paper, we explore using coarse-grained meta-data of Internet traffic on smartphones for depression screening. We develop techniques to identify Internet usage sessions (i.e., time periods when a user is online) and extract a novel set of features based on usage sessions from the Internet traffic meta-data. Our results demonstrate that Internet usage features can reflect the different behavioral characteristics between depressed and non-depressed participants, confirming findings in psychological sciences, which have relied on surveys or questionnaires instead of real Internet traffic as in our study. Furthermore, we develop machine learning based prediction models that use these features to predict depression. Our evaluation shows that Internet usage features can be used for effective depression prediction, leading to F 1 score as high as 0.80.

Grant Report on SCH: Personalized Depression Treatment Supported by Mobile Sensor Analytics.

We report on the newly started project "SCH: Personalized Depression Treatment Supported by Mobile Sensor Analytics". The current best practice guidelines for treating depression call for close monitoring of patients, and periodically adjusting treatment as needed. This project will advance personalized depression treatment by developing a system, DepWatch, that leverages mobile health technologies and machine learning tools. The objective of DepWatch is to assist clinicians with their decision making process in the management of depression. The project comprises two studies. Phase I collects sensory data and other data, e.g., clinical data, ecological momentary assessments (EMA), tolerability and safety data from 250 adult participants with unstable depression symptomatology initiating depression treatment. The data thus collected will be used to develop and validate assessment and prediction models, which will be incorporated into DepWatch system. In Phase II, three clinicians will use DepWatch to support their clinical decision making process. A total of 128 participants under treatment by the three participating clinicians will be recruited for the study. A number of new machine learning techniques will be developed.

Biological predictors of chemotherapy-induced peripheral neuropathy (CIPN): MASCC neurological complications working group overview.

Chemotherapy-induced peripheral neuropathy (CIPN) is a common and debilitating condition associated with a number of chemotherapeutic agents. Drugs commonly implicated in the development of CIPN include platinum agents, taxanes, vinca alkaloids, bortezomib, and thalidomide analogues. As a drug response can vary between individuals, it is hypothesized that an individual's specific genetic variants could impact the regulation of genes involved in drug pharmacokinetics, ion channel functioning, neurotoxicity, and DNA repair, which in turn affect CIPN development and severity. Variations of other molecular markers may also affect the incidence and severity of CIPN. Hence, the objective of this review was to summarize the known biological (molecular and genomic) predictors of CIPN and discuss the means to facilitate progress in this field.

Atomoxetine for the Treatment of ADHD in Incarcerated Adolescents.

Effective interventions for adolescents with attention deficit/hyperactivity disorder (ADHD) in the correctional setting may improve care during incarceration, decrease risk of substance relapse, and reduce recidivism after release from the correctional setting of these individuals. The present report delineates the epidemiology of adolescent ADHD in the correctional setting and its association with substance use disorders and comorbid psychiatric illnesses. Evidence suggests that adolescents with ADHD have a higher risk of arrest and incarceration during adulthood. The present report examines evidence related to efficacy of atomoxetine, a nonstimulant medication for the treatment of adolescent ADHD, and presents data from a case series evaluating the effectiveness of atomoxetine for the treatment of adolescent ADHD in the Connecticut correctional setting. The results from the case series suggest that atomoxetine is effective for the treatment of adolescent ADHD in the context of significant past substance use. In summary, adolescents with ADHD have an elevated risk of incarceration and developing substance use disorders. The present review and pilot case series suggest that atomoxetine is an effective treatment for adolescents with ADHD in the correctional setting.

Adaptation of the Texas Implementation Medication Algorithm for Bipolar Disorder in Adult Female Offenders.

Use of medication algorithms in the correctional setting may facilitate clinical decision making, improve consistency of care, and reduce polypharmacy. The objective of the present study was to evaluate effectiveness of algorithm (Texas Implementation of Medication Algorithm [TIMA])-driven treatment of bipolar disorder (BD) compared with Treatment as Usual (TAU) in the correctional environment. A total of 61 women inmates with BD were randomized to TIMA (n = 30) or TAU (n = 31) and treated over a 12-week period. The outcome measures included measures of BD symptoms, comorbid symptomatology, quality of life, and psychotropic medication utilization. In comparison with TAU, TIMA-driven treatment reduced polypharmacy, decreased overall psychotropic medication utilization, and significantly decreased use of specific classes of psychotropic medication (antipsychotics and antidepressants). This pilot study confirmed the feasibility and benefits of algorithm-driven treatment of BD in the correctional setting, primarily by enhancing appropriate use of evidence-based treatment.

Sleep Disturbances in Schizophrenia.

Sleep disturbances are prevalent in patients with schizophrenia and play a critical role in the morbidity and mortality associated with the illness. Subjective and objective assessments of sleep in patients with schizophrenia have identified certain consistent findings. Findings related to the sleep structure abnormalities have shown correlations with important clinical aspects of the illness. Disruption of specific neurotransmitter systems and dysregulation of clock genes may play a role in the pathophysiology of schizophrenia-related sleep disturbances. Antipsychotic medications play an important role in the treatment of sleep disturbances in these patients and have an impact on their sleep structure.

Sleep Disturbances in Patients with Medical Conditions.

Sleep-wake cycle disturbances are prevalent in patients with medical conditions and frequently present as part of a symptom cluster. Sleep disturbances impair functioning and quality of life, decrease adherence to treatments of the primary medical condition, and increase morbidity and mortality. The pathophysiology of sleep disturbances in these patients involves alterations in immune and neuroendocrine function and shares common pathophysiologic pathways with comorbidities such as fatigue and depression. Emphasis is placed on the evaluation and management of medical and psychiatric comorbidities and other factors contributing to sleep problems. Primary treatments include cognitive-behavioral therapy and pharmacotherapy.

The biology of cancer-related fatigue: a review of the literature.

PURPOSE: Understanding the etiology of cancer-related fatigue (CRF) is critical to identify targets to develop therapies to reduce CRF burden. The goal of this systematic review was to expand on the initial work by the National Cancer Institute CRF Working Group to understand the state of the science related to the biology of CRF and, specifically, to evaluate studies that examined the relationships between biomarkers and CRF and to develop an etiologic model of CRF to guide researchers on pathways to explore or therapeutic targets to investigate. METHODS: This review was completed by the Multinational Association of Supportive Care in Cancer Fatigue Study Group-Biomarker Working Group. The initial search used three terms (biomarkers, fatigue, cancer), which yielded 11,129 articles. After removing duplicates, 9145 articles remained. Titles were assessed for the keywords "cancer" and "fatigue" resulting in 3811 articles. Articles published before 2010 and those with samples <50 were excluded, leaving 75 articles for full-text review. Of the 75 articles, 28 were further excluded for not investigating the associations of biomarkers and CRF. RESULTS: Of the 47 articles reviewed, 25 were cross-sectional and 22 were longitudinal studies. More than half (about 70 %) were published recently (2010-2013). Almost half (45 %) enrolled breast cancer participants. The majority of studies assessed fatigue using self-report questionnaires, and only two studies used clinical parameters to measure fatigue. CONCLUSIONS: The findings from this review suggest that CRF is linked to immune/inflammatory, metabolic, neuroendocrine, and genetic biomarkers. We also identified gaps in knowledge and made recommendations for future research.

Management options for established chemotherapy-induced peripheral neuropathy.

Chemotherapy-induced peripheral neuropathy (CIPN) is a common and debilitating condition associated with a variety of chemotherapeutic agents. Clinicians are cognizant of the negative impact of CIPN on cancer treatment outcomes and patients' psychosocial functioning and quality of life. In an attempt to alleviate this problem, clinicians and patients try various therapeutic interventions, despite limited evidence to support efficacy of these treatments. The rationale for such use is mostly based on the evidence for the treatment options in non-CIPN peripheral neuropathy syndromes, as this area is more robustly studied than is CIPN treatment. In this manuscript, we examine the existing evidence for both CIPN and non-CIPN treatments and develop a summary of the best available evidence with the aim of developing a practical approach to the treatment of CIPN, based on available literature and clinical practice experience.

Medication adherence among female inmates with bipolar disorder: results from a randomized controlled trial.

To describe the differences in medication adherence between 2 groups of inmates in the Connecticut Department of Correction diagnosed with bipolar disorder treated with either the Texas Implementation of Medication Algorithm (TIMA) for Bipolar Disorder or treatment as usual (TAU). Using a prospective longitudinal analysis of secondary data and chart data, a comparison was made between participants who were assigned either to TIMA or TAU and treated for 12 weeks for either Bipolar Disorder Type I or II. A secondary data set containing 12 weeks of medication data was combined with medical chart data, including medication administration records, which were retrospectively reviewed to determine numbers of psychotropic and other medications prescribed, number of doses per day prescribed, number of times the medications were taken, any patterns and reasons for missed doses, and side effects experienced. High rates of psychotropic medication nonadherence were observed among female inmates with bipolar disorder, with the mood stabilizers as the most frequently missed medications. Analyses revealed an interaction of Treatment Condition × Baseline Adherence × Time in Treatment × Biweekly Symptom Severity. Regardless of treatment condition, participants exhibiting high baseline adherence exhibited greater decreases in daily adherence over time; in addition, participants at Time 8 (Weeks 7 and 8) and later exhibited poorer adherence if they had more severe symptoms during those weeks. TIMA participants missed fewer doses than TAU participants. Future research is needed to uncover what factors most significantly contribute to psychotropic medication adherence.

Algorithm-driven pharmacological management of bipolar disorder in Connecticut prisons.

The objective of this study was to assess adaptation of the Texas Implementation of Medication Algorithm (TIMA) for bipolar disorder (BD) in the Connecticut Department of Correction. A nonrandomized sample of 20 males and 20 females, with diagnoses of BD Type I or II, was enrolled in the study. Two TIMA-trained psychiatrists treated the participants over a 12-week period following the TIMA protocol. The primary outcome measure was the Bipolar Disorder Symptom Scale. Secondary outcome measures evaluated global clinical status, comorbid symptomatology, and quality of life. Significant improvement was seen with the primary and secondary outcome measures (p < .001). Subanalyses showed differences in outcomes based on gender and whether a manic or depression algorithm was used. Antidepressant and antipsychotic medication use decreased, with increase in anticonvulsant and anxiolytic medication usage. This pilot study confirmed the effectiveness and benefits of TIMA for BD adaptation in the correctional setting.

Symptom distress associated with biopsy in women with suspect breast lesions.

Purpose. To investigate symptom distress, quality of life, affective states, and inflammatory biomarkers before and after breast biopsy in women undergoing breast biopsy. Methods. A convenience sample of 47 women undergoing breast biopsy was assessed at the pre- and post-biopsy visits. The assessments included evaluation of fatigue, anxiety, depression, sleep disturbances, positive and negative affect, quality of life using validated self report measures, and a blood draw to determine markers of inflammation. Results. At the postbiopsy visit, a total of 15 participants were diagnosed with breast cancer, and 32 participants received negative biopsy result. The mean anxiety and sleep disturbances scores were in the clinically significant range for the total sample and for the biopsy positive (BC+) and biopsy negative (BC-) subgroups at both time points. For both subgroups, anxiety and sleep disturbances scores did not change significantly from pre- to post-biopsy. A subpopulation of participants in both groups reported moderate-to-severe anxiety, depression and fatigue levels at both time points. The inflammatory markers did not show consistent associations with psychosocial symptoms. Conclusions. A subset of participants in BC+ and BC- subgroups experience heightened symptom distress and negative impact on quality of life at both pre- and post-biopsy time points.