bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dynamics of Depressive and Psychomotor Symptoms During Electroconvulsive Therapy in Older Depressive Patients
Authors: Le Droguene Erwan , Bulteau Samuel , Deschamps Thibault , Thomas-Ollivier Véronique , Brichant-Petitjean Clara , Guitteny Marie , Laurin Andrew , Sauvaget Anne 
Published: June 2023

Objective
Electroconvulsive therapy (ECT) is an effective treatment for patients experiencing a major depressive episode, especially older ones. Identification of specific responses within early ECT sessions remains an issue of debate, however. Hence, this pilot study prospectively examined the outcome in terms of depressive signs, symptom by symptom, throughout a course of ECT, concentrating particularly on psychomotor retardation symptoms.

Methods
Nine patients were clinically evaluated several times during the ECT course, before the first session and then weekly (over 3–6 weeks, according to their evolution), by completing the Montgomery-Åsberg Depression Rating Scale (MADRS), the Mini-Mental State Examination test, and the French Retardation Rating Scale for Depression for assessing the severity of psychomotor retardation.

Results
Nonparametric Friedman tests showed significant positive changes in mood disorders during ECT in older depressive patients (mean, −27.3% of initial MADRS total score). Fast improvement in French Retardation Rating Scale for Depression score was observed at t
1 (ie, after 3–4 ECT sessions), whereas a slightly delayed improvement in the MADRS scores was found at t
2 (ie, after 5–6 ECT sessions). Moreover, the scores for items linked to the motor component of psychomotor retardation (eg, gait, postural control, fatigability) were the first to significantly decrease during the first 2 weeks of the ECT course compared with the cognitive component.

Conclusions
Interestingly, participants' concentration on daily functional activities, their interest and fatigability, and their reported state of sadness were the first to progress, representing possible precursor signs of positive patient outcomes after ECT.
Unintended Weight Gain After Treatment of Catatonia With ECT in Autism
Authors: Free Melissa , Pathare Aum , Mahgoub Yassir 
Published: June 2023

Abstract
Catatonia is a syndrome with psychomotor, cognitive, and affective symptoms that has been associated with multiple psychiatric and medical conditions, including autism spectrum disorder. Fluctuations in weight can occur within catatonia by means of poor oral intake, treatment with atypical antipsychotics, and often overlooked psychomotor phenomena. We present a case of a patient with autism spectrum disorder and excessive psychomotor activity due to catatonia who initially experienced weight loss despite maintenance of oral intake and required increased caloric intake to maintain her weight. She was treated with electroconvulsive therapy. After the psychomotor phenomena associated with catatonia reduced, she gained 10 lb (4.5 kg) despite no further alterations to medications or diet. This case demonstrates that excessive psychomotor activity seen in catatonia may increase energy expenditure to the severity of altering caloric requirements and that weight is a salient biomarker to be monitored in catatonia, especially with those who have limited communication abilities.
Subanesthetic Dose of Ketamine Administered Before Each Electroconvulsive Therapy Session Improves Antidepressant and Sleep Quality Outcomes
Authors: Dong Jun , Min Su , Chen Qibing , Qiu HaiTang , Ren Li 
Published: June 2023

Objectives
The main purpose of this trial is to explore the effects of subanesthetic dose of ketamine on sleep quality and symptoms in patients with major depressive disorder undergoing bitemporal electroconvulsive therapy (ECT).

Methods
Seventy-one patients with major depressive disorder and sleep disturbance were randomly divided into 2 groups, namely, the ECT without ketamine group (group ES), receiving routine ECT and saline (3 mL) at each ECT session, and the ECT-assisted ketamine group (group KS), which received ECT and ketamine (3 mL) at each ECT session. The 24 Hamilton Depression Rating Scale was used to assess depressive symptoms and the Chinese Pittsburgh Sleep Quality Index was used to evaluate sleep quality.

Results
The patients in group KS required shorter ECT treatment sessions. Patients in group ES had lower sleep efficiency, longer sleep latency, and required more sleep medication than patients in group KS at the end of the ECT course.

Conclusions
Subanesthetic dose of ketamine improved sleep quality and enhanced ECT therapeutic effects in patients with sleep disturbance.
Antimicrobial and quorum sensing inhibitory activity of epiphytic bacteria isolated from the red alga Halymenia durvillei
Authors: Padayao Mary Hannah Rose , Padayao Francis Reuben Paul , Patalinghug Jenny Marie , Raña Gem Stephen , Yee Jonie , Geraldino Paul John , Quilantang Norman 
Published: June 2023

Halymenia durvillei is a red alga that is commonly utilized in the Philippines as food and as a source of high value natural products for industrial applications. However, there are no studies regarding the microbial community associated with H. durvillei and its potential applications. This study aimed to isolate and identify the epiphytic bacteria of H. durvillei and determine their antimicrobial and quorum sensing inhibitory (QSI) effects. The thalli of H. durvillei were collected at the shores of Santa Fe, Bantayan, Cebu, Philippines. Bacterial isolates were identified using 16S rRNA, and their ethyl acetate (EtOAc) extracts were subjected to antimicrobial susceptibility tests against representative species of yeast, Gram-negative, and Gram-positive bacteria. Their QSI activity against Chromobacterium violaceum was also determined. Fourteen distinct bacterial colonies belonging to four genera, namely, Alteromonas (3), Bacillus (5), Oceanobacillus (1), and Vibrio (5) were successfully isolated and identified. All fourteen bacterial isolates exhibited antibacterial effects. EPB9, identified as B. safensis, consistently showed the strongest inhibition against Escherichia coli, Staphylococcus aureus, and S. epidermidis having minimum inhibitory concentrations (MIC) ranging from 0.0625 – 1.0 mg mL-1. In contrast, all fourteen isolates showed weak antifungal effects. Both B. safensis (EPB 9) and B. australimaris (EPB15) exhibited QSI effects at 100 mg mL-1 showing opaque zones of 3.1 ± 0.9 mm and 3.8 ± 0.4 mm, respectively. This study is the first to isolate and identify the distinct microbial epiphytic bacterial community of H. durvillei and its potential as an abundant resource for new antibacterial and QSI bioactives.
RSYD-BASIC: a bioinformatics pipeline for Routine Sequence analYsis and Data processing of BActerial iSolates for clInical miCrobiology
Authors: Steinke Kat , Thomsen Karina Gravgaard , Hoegh Silje Vermedal , Larsen Sanne Løkkegaard , Kubel Vilhelmsen Karina , Jensen Thøger Gorm , Skov Marianne Nielsine , Sydenham Thomas Vognbjerg 
Published: June 2023

Whole genome sequencing of bacterial isolates is increasingly becoming routine in clinical microbiology; however, subsequent analysis often needs to be started by a bioinformatician even for comprehensive pipelines. With the RSYD-BASIC pipeline, we present a reads-to-results analysis pipeline operated by non-expert users that has already yielded clinically relevant results.
A Mathematical Framework for Understanding Recognition Systems
Authors: Brima Yusuf 
Published: June 2023

AbstractRecognition plays a crucial role in the formation of social bonds, mate selection, kin selection and survival across species. However, a unifying framework for understanding recognition systems in biology is lacking. We, therefore, propose a theoretical framework for biological recognition utilizing basic principles from category theory, information theory, dynamical systems modeling and optimization theory. We define the two types of recognition, individual recognition (IR) and class-level recognition (CR), asfunctorsbetween categories ofstimuliandresponses. IR producesuniqueresponses for each individual, while CR producessharedresponses for multiple individuals of the same class. We identify five conditions –universality, low entropy, unfalsifiability, uniform convergenceandcognitive limit– that must hold for robust IR systems, which we term “signature systems.” Further, we model signature systems asattractor stateswith perspectives from both statistical information processing and dynamical systems. Our framework provides a basis for advancing understanding of the mechanisms underlying biological recognition and its implications for communication and behavior. Overall, the mathematical conceptualization of IR provides a basis for advancing our understanding of communication and the evolution of language.
Pre-trained Multimodal Large Language Model Enhances Dermatological Diagnosis using SkinGPT-4
Authors: Zhou Juexiao , He Xiaonan , Sun Liyuan , Xu Jiannan , Chen Xiuying , Chu Yuetan , Zhou Longxi , Liao Xingyu , Zhang Bin , Gao Xin 
Published: June 2023

AbstractLarge language models (LLMs) are seen to have tremendous potential in advancing medical diagnosis recently. However, it is important to note that most current LLMs are limited to text interaction alone. Meanwhile, the development of multimodal large language models for medical diagnosis is still in its early stages, particularly considering the prevalence of image-based data in the field of medical diagnosis, among which dermatological diagnosis is a very important task as skin and subcutaneous diseases rank high among the leading contributors to the global burden of nonfatal diseases. Inspired by current state-of-the-art multimodal large language models, we present SkinGPT-4, which is the world’s first interactive dermatology diagnostic system based on multimodal large language models. To implement SkinGPT-4, we have designed a new framework that aligned a pre-trained vision transformer with a large language model named Falcon-40B-Instruct, which is based on Falcon. To train SkinGPT-4, we have collected an extensive collection of skin disease images (comprising 52,929 publicly available and proprietary images) along with clinical concepts and doctors’ notes and designed a two-step training strategy. To demonstrate the robustness of SkinGPT-4, we have conducted quantitative evaluations on 150 real-life cases, which were independently reviewed by certified dermatologists. With SkinGPT-4, users could upload their own skin photos for diagnosis, and the system could autonomously evaluate the images, identifies the characteristics and categories of the skin conditions, performs in-depth analysis, and provides interactive treatment recommendations. Meanwhile, SkinGPT-4’s local deployment capability and commitment to user privacy also render it an appealing choice for patients. Though SkinGPT-4 is not a substitute for doctors, it could enhance users’ comprehension of their medical conditions, facilitate improve communication between patients and doctors, expedite the diagnostic process for dermatologists, facilitate triage, and potentially promote human-centred care and healthcare equity in underdeveloped areas. In summary, SkinGPT-4 represents a significant leap forward in the field of dermatology diagnosis in the era of large language models and a valuable exploration of multimodal large language models in medical diagnosis.
Convergent Insulin and TGF-β signalling drives cancer cachexia by promoting aberrant fatbody ECM accumulation in aDrosophilatumour model
Authors: Bakopoulos Daniel , Golenkina Sofia , Dark Callum , Christie Elizabeth L , Sánchez-Sánchez Besaiz J. , Stramer Brian M. , Cheng Louise Y 
Published: June 2023

AbstractCancer cachexia is a wasting disease suffered by advanced stage cancer patients and ultimately causes ∼30% of cancer mortalities. Clinical observations have shown that extracellular matrix (ECM) remodelling which leads to fibrosis in the adipose tissue is a key feature of cancer cachexia. However, the molecular regulators of adipose ECM remodelling are not known and how this leads to muscle wasting is unclear. In this study, using aDrosophilacachexia model, we found that in the adipose tissue of both wildtype and tumour bearing animals, insulin and TGF-β signalling converge via a BMP antagonistshort gastrulation(sog) to regulate ECM remodelling. In tumour bearing animals, the aberrant ECM accumulation in the fatbody, contributes towards muscle detachment by preventing ECM secretion and subsequently depleting muscles of fatbody-secreted ECM proteins. Strikingly, activation of insulin signalling, inhibition of TGF-β signalling, or modulation of ECM secretion via SPARC or Rab10 in the fatbody, was able to rescue tissue wasting in the presence of tumour. Together, our study highlights the importance of adipose ECM remodelling in the context of cancer cachexia.
Alterations of lipid homeostasis in serum and white adipose tissue in morbid obese patients are partly reversed by the bariatric surgery
Authors: Sinturel Flore , Chera Simona , Brulhart-Meynet Marie-Claude , Montoya Jonathan Paz , Lefai Etienne , Jornayvaz François R. , D’Angelo Giovanni , Jung Minoa Karin , Pataky Zoltan , Riezman Howard , Dibner Charna 
Published: June 2023

AbstractGastric Bypass surgery (GBS) represents a well-established approach to counteract human morbid obesity and its related comorbidities in modern countries. Beside its beneficial effect on weight loss and glucose homeostasis, emerging evidence suggests that GBS impacts on the circulating levels of phospho- and sphingolipids. However, long-term effects of GBS on lipid metabolism have not been explored. Thereby, we aimed to unveil to what extent GBS improves lipid homeostasis in serum and tissues from morbid obese individuals.To investigate alterations in lipidomic signatures associated with massive weight loss following GBS in morbid obese patients, we employed direct infusion tandem mass spectrometry (MS) allowing to quantify a wide range of lipid metabolites in serum and subcutaneous adipose tissue (SAT) samples. Systematic lipidomic analyses were conducted in samples collected in a longitudinal cohort of patients (cohort 1, n = 11) prior to GBS, and one year following the surgery. These novel data were cross compared with our recent lipidomic analyses conducted by the same approach in an independent cohort of morbid obese patients and lean controls, where serum and visceral adipose tissue (VAT) lipids were analysed (cohort 2, n = 39).Over 400 phospholipid and sphingolipid species have been quantified in serum and SAT (cohort 1), allowing to establish detailed lipidomic signatures associated with morbid obesity in a tissue-specific manner. Concomitant with weight loss and improvement of metabolic parameters, a massive rearrangement of lipid metabolites was observed one year following GBS. Strikingly, a substantial reduction of ceramide levels and increased amount of hexosylceramides were detected in both serum and SAT. The comparison of these new lipidomic profiles with the serum and VAT lipidomes established from lean and morbid obese subjects (cohort 2) revealed that GBS partly restored the lipid alterations associated with morbid obesity.Our study provides the first systematic analysis of the long-term lipid homeostasis modifications upon GBS in humans SAT and serum and demonstrates that lipid metabolism alterations associated with morbid obesity might be partly reversed by GBS.The research protocol was registered with the Protocol Registration and Results System atClinicalTrial.gov[NCT03029572].
Detection of multidrug resistant organisms of concern includingS. maltophiliaandB. cepaciaat a referral hospital in Kenya
Authors: Kimani Racheal , Maina Patrick , Kamita Moses , Mbogo David , Mutai Winnie , Ayodo Charchil , Suliman Essuman , Kanoi Bernard N. , Gitaka Jesse 
Published: June 2023

AbstractIt is important to monitor of antibiotic susceptibility patterns of bacteria in clinical settings periodically to ascertain the current trends as well as re-establish empirical therapy. This study aimed to determine bacterial contaminants and their antimicrobial susceptibility patterns from medical equipment, inanimate surfaces and clinical samples isolated from Thika Level V Hospital (TLVH). Three hundred and five samples were collected and comprised of urine, pus swabs, catheter swabs, stool and environmental samples. Bacterial identification and antimicrobial susceptibility testing were done on VITEK 2 and disc diffusion respectively.Coagulase negativeStaphylococci(28 /160, 17.5%) were the most isolated species from patients followed byE. coli(22 /160 13.8%) andS. aureus(22/160, 13.8%). The bed rail was the most contaminated surface withS. aureusat (6/42)14.2%. The clinical sample that yielded the highest number of pathogens was pus (92/160). Trauma patients had the largest proportion of isolates (67/160, 41.8%). Bacteria recovered from this study demonstrated high levels of resistance especially enteric bacteria. Extended Spectrum Beta Lactamase phenotype was noted in 29/44 (65.9%) enteric isolates. Although ESBL genetic confirmatory studies are needed, this study shows that there is an urgent need for actions that mitigate the spread of antibiotics resistant bacteria.
Objective Monitoring of Loneliness Levels using Smart Devices: A Multi-Device Approach for Mental Health Applications
Authors: Jafarlou Salar , Azimi Iman , Lai Jocelyn , Wang Yuning , Labbaf Sina , Nguyen Brenda , Qureshi Hana , Marcotullio Christopher , Borelli Jessica L. , Dutt Nikil D. , Rahmani Amir M. 
Published: June 2023

AbstractLoneliness is linked to wide ranging physical and mental health problems, including increased rates of mortality. Understanding how loneliness manifests is important for targeted public health treatment and intervention. With advances in mobile sending and wearable technologies, it is possible to collect data on human phenomena in a continuous and uninterrupted way. In doing so, such approaches can be used to monitor physiological and behavioral aspects relevant to an individual’s loneliness. In this study, we proposed a method for continuous detection of loneliness using fully objective data from smart devices and passive mobile sensing. We also investigated whether physiological and behavioral features differed in their importance in predicting loneliness across individuals. Finally, we examined how informative data from each device is for loneliness detection tasks. We assessed subjective feelings of loneliness while monitoring behavioral and physiological patterns in 30 college students over a 2-month period. We used smartphones to monitor behavioral patterns (e.g., location changes, type of notifications, in-coming and out-going calls/text messages) and smart watches and rings to monitor physiology and sleep patterns (e.g., heart-rate, heart-rate variability, sleep duration). We also collected participants’ loneliness feeling scales multiple times a day through a questionnaire app on their phone. Using the data collected from their devices, we trained a random forest machine learning based model to detect loneliness levels. We found support for loneliness prediction using a multi-device and fully-objective approach. Furthermore, behavioral data collected by smartphones generally were the most important features across all participants. The study provides promising results for using objective data to monitor mental health indicators, which could provide a continuous and uninterrupted source of information in mental healthcare applications.
Cognitive Dynamics Estimation: A whole-brain spatial regression paradigm for extracting the temporal dynamics of cognitive processes
Authors: Koyama Yutaro , Yamamoto Tetsuya , Hirayama Jun-ichiro , Jimura Koji , Sadato Norihiro , Chikazoe Junichi 
Published: June 2023

AbstractFunctional MRI (fMRI) has been instrumental in understanding how cognitive processes are spatially mapped in the brain, yielding insights about brain regions and functions. However, in case the orthogonality of behavioral or stimulus timing is not guaranteed, the estimated brain maps fail to dissociate each cognitive process, and the resultant maps become unstable. Also, the brain mapping exercise can not provide temporal information on the cognitive process. Here we propose a qualitatively different approach to fMRI analysis, named Cognitive Dynamics Estimation (CDE), that estimates how multiple cognitive processes change over time even when behavior or stimulus logs are unavailable. This method transposes the conventional brain mapping; the brain activity pattern at each time point is subject to regression analysis with data-driven maps of cognitive processes as regressors, resulting in the time series of cognitive processes. The estimated time series captured the fluctuation of intensity and timing of cognitive processes on a trial-by-trial basis, which conventional analysis could not capture. Notably, the estimated time series predicted participants’ cognitive ability to perform each psychological task. As an addition to our fMRI analytic toolkit, these results suggest the potential for CDE to elucidate underexplored cognitive phenomena, especially in the temporal domain.HighlightsWe propose a novel fMRI analysis that are equivalently effective to the brain mapping approachThe temporal dynamics of multiple cognitive processes were captured on a trial-by-trial basis.This analysis can be applied data-driven even when the researchers do not have any hypothesis about what kind of cognitive processes are recruited in the task.This analysis can be applied even when the cognitive processes are strongly correlated in the temporal domain.The estimated temporal dynamics well predicted individuals’ cognitive ability required for each task.
Improved short-channel regression for mapping resting-state functional connectivity networks using functional near-infrared spectroscopy
Authors: Novi S. L. , Abdalmalak A. , Kazazian K. , Norton L. , Debicki D. B. , Mesquita R. C. , Owen A. M. 
Published: June 2023

AbstractResting-state functional connectivity (rsFC) is an attractive biomarker of brain function that can vary with brain injury. The simplicity of resting-state protocols coupled with the main features of functional near-infrared spectroscopy (fNIRS), such as portability and versatility, can facilitate the monitoring of unresponsive patients in acute settings at the bedside. However, accurately mapping rsFC networks is challenging due to signal contamination from non-neural components, such as scalp hemodynamics and systemic physiology. Physiological noise may be mitigated through the use of short channels which may be able to provide sufficient information to eliminate the need for additional measurement devices, decreasing the complexity of the experimental setup. To this end, we examined the extent to which systemic physiology is embedded in the short-channel data and improved short-channel regression to account for temporal heterogeneity in the scalp hemodynamics. Our findings indicate that using temporal shifts in the short-channel data increases the agreement, by 70% on average, between short-channel regression and regression that includes short channels and physiological recordings. Overall, this method decreases the need for additional physiological recordings when mapping rsFC networks, providing a viable alternative when such measurements are not available or feasible.