Lenvatinib­based treatment regimens in conversion therapy of unresectable hepatocellular carcinoma: A systematic review and meta­analysis.

Hepatocellular carcinoma (HCC) is a malignancy associated with high morbidity and mortality rates. Conversion therapy provides patients with unresectable HCC (uHCC) the opportunity to undergo radical treatment and achieve long-term survival. Despite accumulating evidence regarding the efficacy of conversion therapy, the optimal treatment approach for such therapy remains uncertain. Lenvatinib (LEN) has shown efficacy and tolerable rates of adverse events (AEs) when applied in combination with immune checkpoint inhibitors (ICIs) or locoregional therapy (LRT) over the past decade. Therefore, the present meta-analysis was performed to systematically assess the safety and efficacy of LEN-based treatment regimens in conversion therapies for uHCC. Data on outcomes, including the conversion rate, objective response rate (ORR), disease control rate (DCR) and AE incidence in patients with uHCC, were collected. A systematic literature search was performed using MEDLINE, Embase, Web of Science and Cochrane Library databases, up to the date of September 1, 2023. In total, 16 studies, encompassing a total of 1,650 cases of uHCC, were included in the final meta-analysis. The pooled conversion rates for LEN alone, LEN + ICI, LEN + LRT and LEN + ICI + LRT were calculated to be 0.04 (95% CI, 0.00-0.07; I2=77%), 0.23 (95% CI, 0.16-0.30; I2=66%), 0.14 (95% CI, 0.10-0.18; I2=0%) and 0.35 (95% CI, 0.23-0.47; I2=88%), respectively. The pooled ORRs for LEN alone, LEN + ICI, LEN + LRT and LEN + ICI + LRT were found to be 0.45 (95% CI, 0.23-0.67; I2=96%), 0.49 (95% CI, 0.39-0.60; I2=78%), 0.43 (95% CI, 0.24-0.62; I2=88%) and 0.69 (95% CI, 0.56-0.82; I2=92%), respectively. The pooled DCRs for LEN alone, LEN + ICI, LEN + LRT and LEN + ICI + LRT were observed to be 0.77 (95% CI, 0.73-0.81; I2=23%), 0.82 (95% CI, 0.69-0.95; I2=90%), 0.67 (95% CI, 0.39-0.94; I2=94%) and 0.87 (95% CI, 0.82-0.93; I2=67%), respectively. The pooled grade ≥3 AEs for LEN alone, LEN + ICI, LEN + LRT and LEN + ICI + LRT were 0.25 (95% CI, 0.14-0.36; I2=89%), 0.43 (95% CI, 0.34-0.53; I2=23%), 0.42 (95% CI, 0.19-0.66; I2=81%) and 0.35 (95% CI, 0.17-0.54; I2=94%), respectively. These findings suggested that LEN-based combination strategies may confer efficacy and acceptable tolerability for patients with uHCC. In particular, LEN + ICI, with or without LRT, appears to represent a highly effective conversion regimen, with an acceptable conversion rate and well-characterized safety profile.

Left coronary sinus of valsalva aneurysm dissecting into interventricular septum: a case report.

BACKGROUND: Sinus of Valsalva aneurysm (SVA) is an extremely rare condition, and its rupture causes acute symptoms such as chest pain and dyspnea. Ruptured SVA is frequently associated with other congenital defects. CASE PRESENTATION: A 37-year-old male presented with SVA originating from the left coronary sinus that ruptured into the interventricular septum. SVA was diagnosed by echocardiography, cardiac computed tomography and magnetic resonance imaging, and confirmed during the operation. CONCLUSIONS: SVA is a rare cardiac abnormality which can lead to severe clinical symptoms upon rupture. Immediate surgery is necessary to repair the ruptured SVA.

Association of sarcopenia with liver fibrosis and steatohepatitis in non-alcoholic fatty liver disease: protocol for a systematic review and meta-analysis.

INTRODUCTION: Non-alcoholic fatty liver disease (NAFLD) has become the most common chronic liver disorder over the last four decades, more evidence shows a high prevalence of sarcopenia in NAFLD that may influence disease severity. This meta-analysis aims to determine the association of sarcopenia with liver fibrosis and steatohepatitis in NAFLD. METHODS AND ANALYSIS: We will conduct the literature search using Medline (via PubMed), Web of Science databases, EMBASE, Cochrane Central Register of Controlled Trials and the Cochrane Database of Systematic Reviews (from the date of inception to 1 May 2022). There will be no restriction to the publication year. Two reviewers will independently screen the articles and abstract key study characteristics. The outcome of this meta-analysis is the strength of association of sarcopenia with liver fibrosis and steatohepatitis in NAFLD. The STATA (V.14, StataCorp, 2015) will be used to carry out the statistical analysis. Comprehensive evaluation of bias risk and heterogeneity will be performed before data synthesis. Also, consistency and evidence quality will be assessed. ETHICS AND DISSEMINATION: There will be no need of ethics approval as this systematic review is summary and analysis of existing literature. Final results may be presented in international conferences or a peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD42022322685.

Language patterns in Japanese patients with Alzheimer disease: A machine learning approach.

AIM: The authors applied natural language processing and machine learning to explore the disease-related language patterns that warrant objective measures for assessing language ability in Japanese patients with Alzheimer disease (AD), while most previous studies have used large publicly available data sets in Euro-American languages. METHODS: The authors obtained 276 speech samples from 42 patients with AD and 52 healthy controls, aged 50 years or older. A natural language processing library for Python was used, spaCy, with an add-on library, GiNZA, which is a Japanese parser based on Universal Dependencies designed to facilitate multilingual parser development. The authors used eXtreme Gradient Boosting for our classification algorithm. Each unit of part-of-speech and dependency was tagged and counted to create features such as tag-frequency and tag-to-tag transition-frequency. Each feature's importance was computed during the 100-fold repeated random subsampling validation and averaged. RESULTS: The model resulted in an accuracy of 0.84 (SD = 0.06), and an area under the curve of 0.90 (SD = 0.03). Among the features that were important for such predictions, seven of the top 10 features were related to part-of-speech, while the remaining three were related to dependency. A box plot analysis demonstrated that the appearance rates of content words-related features were lower among the patients, whereas those with stagnation-related features were higher. CONCLUSION: The current study demonstrated a promising level of accuracy for predicting AD and found the language patterns corresponding to the type of lexical-semantic decline known as 'empty speech', which is regarded as a characteristic of AD.

Understanding psychiatric illness through natural language processing (UNDERPIN): Rationale, design, and methodology.

Introduction: Psychiatric disorders are diagnosed through observations of psychiatrists according to diagnostic criteria such as the DSM-5. Such observations, however, are mainly based on each psychiatrist's level of experience and often lack objectivity, potentially leading to disagreements among psychiatrists. In contrast, specific linguistic features can be observed in some psychiatric disorders, such as a loosening of associations in schizophrenia. Some studies explored biomarkers, but biomarkers have yet to be used in clinical practice. Aim: The purposes of this study are to create a large dataset of Japanese speech data labeled with detailed information on psychiatric disorders and neurocognitive disorders to quantify the linguistic features of those disorders using natural language processing and, finally, to develop objective and easy-to-use biomarkers for diagnosing and assessing the severity of them. Methods: This study will have a multi-center prospective design. The DSM-5 or ICD-11 criteria for major depressive disorder, bipolar disorder, schizophrenia, and anxiety disorder and for major and minor neurocognitive disorders will be regarded as the inclusion criteria for the psychiatric disorder samples. For the healthy subjects, the absence of a history of psychiatric disorders will be confirmed using the Mini-International Neuropsychiatric Interview (M.I.N.I.). The absence of current cognitive decline will be confirmed using the Mini-Mental State Examination (MMSE). A psychiatrist or psychologist will conduct 30-to-60-min interviews with each participant; these interviews will include free conversation, picture-description task, and story-telling task, all of which will be recorded using a microphone headset. In addition, the severity of disorders will be assessed using clinical rating scales. Data will be collected from each participant at least twice during the study period and up to a maximum of five times at an interval of at least one month. Discussion: This study is unique in its large sample size and the novelty of its method, and has potential for applications in many fields. We have some challenges regarding inter-rater reliability and the linguistic peculiarities of Japanese. As of September 2022, we have collected a total of >1000 records from >400 participants. To the best of our knowledge, this data sample is one of the largest in this field. Clinical Trial Registration: Identifier: UMIN000032141.

Clinical prognosis of intraoperative blood salvage autotransfusion in liver transplantation for hepatocellular carcinoma: A systematic review and meta-analysis.

Background: Intraoperative blood salvage autotransfusion(IBSA) has been widely used in a variety of surgeries, but the use of IBSA in hepatocellular carcinoma (HCC) patients undergoing liver transplantation (LT) is controversial. Numerous studies have reported that IBSA used during LT for HCC is not associated with adverse oncologic outcomes. This systematic review and meta-analysis aims to estimate the clinical prognosis of IBSA for patients with H+CC undergoing LT. Methods: MEDLINE, Embase, Web of Science, and Cochrane Library were searched for articles describing IBSA in HCC patients undergoing LT from the date of inception until May 1, 2022, and a meta-analysis was performed. Study heterogeneity was assessed by I2 test. Publication bias was evaluated by funnel plots, Egger's and Begg's test. Results: 12 studies enrolling a total of 2253 cases (1374 IBSA and 879 non-IBSA cases) are included in this meta-analysis. The recurrence rate(RR) at 5-year(OR=0.75; 95%CI, 0.59-0.95; P=0.02) and 7-year(OR=0.65; 95%CI, 0.55-0.97; P=0.03) in the IBSA group is slightly lower than non-IBSA group. There are no significant differences in the 1-year RR(OR=0.77; 95% CI, 0.56-1.06; P=0.10), 3-years RR (OR=0.79; 95% CI, 0.62-1.01; P=0.06),1-year overall survival outcome(OS) (OR=0.90; 95% CI, 0.63-1.28; P=0.57), 3-year OS(OR=1.16; 95% CI, 0.83-1.62; P=0.38), 5-year OS(OR=1.04; 95% CI, 0.76-1.40; P=0.82),1-year disease-free survival rate(DFS) (OR=0.80; 95%CI, 0.49-1.30; P=0.36), 3-year DFS(OR=0.99; 95%CI, 0.64-1.55; P=0.98), and 5-year DFS(OR=0.88; 95%CI, 0.60-1.28; P=0.50). Subgroup analysis shows a difference in the use of leukocyte depletion filters group of 5-year RR(OR=0.73; 95%CI, 0.55-0.96; P=0.03). No significant differences are found in other subgroups. Conclusions: IBSA provides comparable survival outcomes relative to allogeneic blood transfusion and does not increase the tumor recurrence for HCC patients after LT. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42022295479.

The core autophagy protein ATG5 controls the polarity of the Golgi apparatus and insulin secretion of pancreatic beta cells.

Pancreatic beta cells are insulin-producing cells that are structurally and functionally polarized in the islets of Langerhans. The organization and position of the Golgi complex play a key role in maintaining a polarized cell state, but the factors and molecular mechanisms determining the Golgi polarization of pancreatic beta cells are still unknown. In the current study, using pancreatic beta cell-specific Atg5 knockout mice, we found that Atg5, an essential gene for autophagy, plays a pivotal role in regulating Golgi integrity and polarization by affecting the expression of genes involved in vesicle transport. Deletion of Atg5 led to endoplasmic reticulum (ER) stress and impaired the distribution of proinsulin and insulin secretion of pancreatic beta cells, which further exacerbates diabetes. These results contribute to a comprehensive understanding of autophagy-mediated Golgi polarization and its regulation of the function of pancreatic beta cells.

Development of medical device software for the screening and assessment of depression severity using data collected from a wristband-type wearable device: SWIFT study protocol.

Introduction: Few biomarkers can be used clinically to diagnose and assess the severity of depression. However, a decrease in activity and sleep efficiency can be observed in depressed patients, and recent technological developments have made it possible to measure these changes. In addition, physiological changes, such as heart rate variability, can be used to distinguish depressed patients from normal persons; these parameters can be used to improve diagnostic accuracy. The proposed research will explore and construct machine learning models capable of detecting depressive episodes and assessing their severity using data collected from wristband-type wearable devices. Methods and analysis: Patients with depressive symptoms and healthy subjects will wear a wristband-type wearable device for 7 days; data on triaxial acceleration, pulse rate, skin temperature, and ultraviolet light will be collected. On the seventh day of wearing, the severity of depressive episodes will be assessed using Structured Clinical Interview for DSM-5 (SCID-5), Hamilton Depression Rating Scale (HAMD), and other scales. Data for up to five 7-day periods of device wearing will be collected from each subject. Using wearable device data associated with clinical symptoms as supervisory data, we will explore and build a machine learning model capable of identifying the presence or absence of depressive episodes and predicting the HAMD scores for an unknown data set. Discussion: Our machine learning model could improve the clinical diagnosis and management of depression through the use of a wearable medical device. Clinical trial registration: [https://jrct.niph.go.jp/latest-detail/jRCT1031210478], identifier [jRCT1031210478].

Exosomes secreted from human umbilical cord mesenchymal stem cells promote pancreatic ductal adenocarcinoma growth by transferring miR-100-5p.

PURPOSE: Although human umbilical cord mesenchymal stem cells (hucMSCs) can contribute to the growth of tumors, including pancreatic ductal adenocarcinoma (PDAC), however, little is known about the exact mechanisms by which the exosomes secreted from hucMSCs (hucMSCs-exo) have an oncogenic effect on the physiopathology of PDAC. The effects of hucMSCs on tumor development are attributed to hucMSCs-exo, which deliver unique proteins and miRNAs to cancer cells. METHODS: HucMSCs and exosomes were isolated and confirmed via transmission electron microscopy, nanoparticle tracking analysis and western blot. The nude mice were inoculated subcutaneously on both flanks with human pancreatic cancer Panc-1 cells (1 × 106), and hucMSCs-exo were directly administered via intratumoral injection once a day for three days each week. Cell proliferation assays were performed using a Cell Counting Kit-8 assay and the cell invasion assay was performed using Transwell assay. The miRNA data were predicted and analyzed by miRanda software. The analysis of the target genes of the miRNAs was proformed with the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. RESULTS: Firstly, we observed that hucMSCs-exo promoted Panc-1 and BxPC3 cell growth by increasing proliferation and migration in vitro. Secondly, in a xenograft tumor model, hucMSCs-exo increased the growth of Panc-1 cells. Thirdly, high-throughput sequencing of hucMSCs-exo showed that hsa-miR-148a-3p, hsa-miR-100-5p, hsa-miR-143-3p, hsa-miR-21-5p and hsa-miR-92a-3p were highly expressed. For the five identified miRNAs, 1308 target genes were predicted by miRanda software. From the GO and KEGG analyses of the target genes of the identified miRNAs, it was found that the main GO function was the regulation of cellular glucuronidation, and the main KEGG metabolic pathway involved the metabolism of ascorbic acid and aldehyde acid. These processes are related to the occurrence and development of pancreatic cancer. Finally, we observed that miR-100-5p promoted Panc-1 and BxPC3 cell growth in vitro and in vivo. CONCLUSION: Here, by utilizing exosomes secreted from hucMSCs, we systematically investigated the effects of hucMSCs-exo on PDAC growth in vitro and in vivo for the first time. Building on these results, we provided new insights into the role of hucMSCs-exo in the PDAC growth and revealed the attractive communication between hucMSCs and PDAC cells that occurs through MSCs-exosomes-miRNAs.

MetaVelvet-DL: a MetaVelvet deep learning extension for de novo metagenome assembly.

BACKGROUND: The increasing use of whole metagenome sequencing has spurred the need to improve de novo assemblers to facilitate the discovery of unknown species and the analysis of their genomic functions. MetaVelvet-SL is a short-read de novo metagenome assembler that partitions a multi-species de Bruijn graph into single-species sub-graphs. This study aimed to improve the performance of MetaVelvet-SL by using a deep learning-based model to predict the partition nodes in a multi-species de Bruijn graph. RESULTS: This study showed that the recent advances in deep learning offer the opportunity to better exploit sequence information and differentiate genomes of different species in a metagenomic sample. We developed an extension to MetaVelvet-SL, which we named MetaVelvet-DL, that builds an end-to-end architecture using Convolutional Neural Network and Long Short-Term Memory units. The deep learning model in MetaVelvet-DL can more accurately predict how to partition a de Bruijn graph than the Support Vector Machine-based model in MetaVelvet-SL can. Assembly of the Critical Assessment of Metagenome Interpretation (CAMI) dataset showed that after removing chimeric assemblies, MetaVelvet-DL produced longer single-species contigs, with less misassembled contigs than MetaVelvet-SL did. CONCLUSIONS: MetaVelvet-DL provides more accurate de novo assemblies of whole metagenome data. The authors believe that this improvement can help in furthering the understanding of microbiomes by providing a more accurate description of the metagenomic samples under analysis.

Hyperglycemia-Induced Dysregulated Fusion Intermediates in Insulin-Secreting Cells Visualized by Super-Resolution Microscopy.

Impaired insulin release is a hallmark of type 2 diabetes and is closely related to chronically elevated glucose concentrations, known as "glucotoxicity." However, the molecular mechanisms by which glucotoxicity impairs insulin secretion remain poorly understood. In addition to known kiss-and-run and kiss-and-stay fusion events in INS-1 cells, ultrafast Hessian structured illumination microscopy (Hessian SIM) enables full fusion to be categorized according to the newly identified structures, such as ring fusion (those with enlarged pores) or dot fusion (those without apparent pores). In addition, we identified four fusion intermediates during insulin exocytosis: initial pore opening, vesicle collapse, enlarged pore formation, and final pore dilation. Long-term incubation in supraphysiological doses of glucose reduced exocytosis in general and increased the occurrence of kiss-and-run events at the expense of reduced full fusion. In addition, hyperglycemia delayed pore opening, vesicle collapse, and enlarged pore formation in full fusion events. It also reduced the size of apparently enlarged pores, all of which contributed to the compromised insulin secretion. These phenotypes were mostly due to the hyperglycemia-induced reduction in syntaxin-1A (Stx-1A) and SNAP-25 protein, since they could be recapitulated by the knockdown of endogenous Stx-1A and SNAP-25. These findings suggest essential roles for the vesicle fusion type and intermediates in regulating insulin secretion from pancreatic beta cells in normal and disease conditions.

Extracellular vesicles and pancreatitis: mechanisms, status and perspectives.

Comprehensive reviews and large population-based cohort studies have played an important role in the diagnosis and treatment of pancreatitis and its sequelae. The incidence and mortality of pancreatitis have been reduced significantly due to substantial advancements in the pathophysiological mechanisms and clinically effective treatments. The study of extracellular vesicles (EVs) has the potential to identify cell-to-cell communication in diseases such as pancreatitis. Exosomes are a subset of EVs with an average diameter of 50~150 nm. Their diverse and unique constituents include nucleic acids, proteins, and lipids, which can be transferred to trigger phenotypic changes of recipient cells. In recent years, many reports have indicated the role of EVs in pancreatitis, including acute pancreatitis, chronic pancreatitis and autoimmune pancreatitis, suggesting their potential influence on the development and progression of pancreatitis. Plasma exosomes of acute pancreatitis can effectively reach the alveolar cavity and activate alveolar macrophages to cause acute lung injury. Furthermore, upregulated exosomal miRNAs can be used as biomarkers for acute pancreatitis. Here, we summarized the current understanding of EVs in pancreatitis with an emphasis on their biological roles and their potential use as diagnostic biomarkers and therapeutic agents for this disease.

Using speech recognition technology to investigate the association between timing-related speech features and depression severity.

BACKGROUND: There are no reliable and validated objective biomarkers for the assessment of depression severity. We aimed to investigate the association between depression severity and timing-related speech features using speech recognition technology. METHOD: Patients with major depressive disorder (MDD), those with bipolar disorder (BP), and healthy controls (HC) were asked to engage in a non-structured interview with research psychologists. Using automated speech recognition technology, we measured three timing-related speech features: speech rate, pause time, and response time. The severity of depression was assessed using the Hamilton Depression Rating Scale 17-item version (HAMD-17). We conducted the current study to answer the following questions: 1) Are there differences in speech features among MDD, BP, and HC? 2) Do speech features correlate with depression severity? 3) Do changes in speech features correlate with within-subject changes in depression severity? RESULTS: We collected 1058 data sets from 241 individuals for the study (97 MDD, 68 BP, and 76 HC). There were significant differences in speech features among groups; depressed patients showed slower speech rate, longer pause time, and longer response time than HC. All timing-related speech features showed significant associations with HAMD-17 total scores. Longitudinal changes in speech rate correlated with changes in HAMD-17 total scores. CONCLUSIONS: Depressed individuals showed longer response time, longer pause time, and slower speech rate than healthy individuals, all of which were suggestive of psychomotor retardation. Our study suggests that speech features could be used as objective biomarkers for the assessment of depression severity.

The project for objective measures using computational psychiatry technology (PROMPT): Rationale, design, and methodology.

INTRODUCTION: Depressive and neurocognitive disorders are debilitating conditions that account for the leading causes of years lived with disability worldwide. However, there are no biomarkers that are objective or easy-to-obtain in daily clinical practice, which leads to difficulties in assessing treatment response and developing new drugs. New technology allows quantification of features that clinicians perceive as reflective of disorder severity, such as facial expressions, phonic/speech information, body motion, daily activity, and sleep. METHODS: Major depressive disorder, bipolar disorder, and major and minor neurocognitive disorders as well as healthy controls are recruited for the study. A psychiatrist/psychologist conducts conversational 10-min interviews with participants ≤10 times within up to five years of follow-up. Interviews are recorded using RGB and infrared cameras, and an array microphone. As an option, participants are asked to wear wrist-band type devices during the observational period. Various software is used to process the raw video, voice, infrared, and wearable device data. A machine learning approach is used to predict the presence of symptoms, severity, and the improvement/deterioration of symptoms. DISCUSSION: The overall goal of this proposed study, the Project for Objective Measures Using Computational Psychiatry Technology (PROMPT), is to develop objective, noninvasive, and easy-to-use biomarkers for assessing the severity of depressive and neurocognitive disorders in the hopes of guiding decision-making in clinical settings as well as reducing the risk of clinical trial failure. Challenges may include the large variability of samples, which makes it difficult to extract the features that commonly reflect disorder severity. TRIAL REGISTRATION: UMIN000021396, University Hospital Medical Information Network (UMIN).

Speech Quality Feature Analysis for Classification of Depression and Dementia Patients.

Loss of cognitive ability is commonly associated with dementia, a broad category of progressive brain diseases. However, major depressive disorder may also cause temporary deterioration of one's cognition known as pseudodementia. Differentiating a true dementia and pseudodementia is still difficult even for an experienced clinician and extensive and careful examinations must be performed. Although mental disorders such as depression and dementia have been studied, there is still no solution for shorter and undemanding pseudodementia screening. This study inspects the distribution and statistical characteristics from both dementia patient and depression patient, and compared them. It is found that some acoustic features were shared in both dementia and depression, albeit their correlation was reversed. Statistical significance was also found when comparing the features. Additionally, the possibility of utilizing machine learning for automatic pseudodementia screening was explored. The machine learning part includes feature selection using LASSO algorithm and support vector machine (SVM) with linear kernel as the predictive model with age-matched symptomatic depression patient and dementia patient as the database. High accuracy, sensitivity, and specificity was obtained in both training session and testing session. The resulting model was also tested against other datasets that were not included and still performs considerably well. These results imply that dementia and depression might be both detected and differentiated based on acoustic features alone. Automated screening is also possible based on the high accuracy of machine learning results.

Mitochondria determine the sequential propagation of the calcium macrodomains revealed by the super-resolution calcium lantern imaging.

Despite the wide application of super-resolution (SR) microscopy in biological studies of cells, the technology is rarely used to monitor functional changes in live cells. By combining fast spinning disc-confocal structured illumination microscopy (SD-SIM) with loading of cytosolic fluorescent Ca2+ indicators, we have developed an SR method for visualization of regional Ca2+ dynamics and related cellular organelle morphology and dynamics, termed SR calcium lantern imaging. In COS-7 cells stimulated with ATP, we have identified various calcium macrodomains characterized by different types of Ca2+ release from endoplasmic reticulum (ER) stores. Finally, we demonstrated various roles of mitochondria in mediating calcium signals from different sources; while mitochondria can globally potentiate the Ca2+ entry associated with store release, mitochondria also locally control Ca2+ release from the neighboring ER stores and assist in their refilling processes.

Predicting Individual Remission After Electroconvulsive Therapy Based on Structural Magnetic Resonance Imaging: A Machine Learning Approach.

OBJECTIVE: To identify important clinical or imaging features predictive of an individual's response to electroconvulsive therapy (ECT) by utilizing a machine learning approach. METHODS: Twenty-seven depressed patients who received ECT were recruited. Clinical demographics and pretreatment structural magnetic resonance imaging (MRI) data were used as candidate features to build models to predict remission and post-ECT Hamilton Depression Rating Scale scores. Support vector machine and support vector regression with elastic-net regularization were used to build models using (i) only clinical features, (ii) only MRI features, and (iii) both clinical and MRI features. Consistently selected features across all individuals were identified through leave-one-out cross-validation. RESULTS: Compared with models that include only clinical variables, the models including MRI data improved the prediction of ECT remission: the prediction accuracy improved from 70% to 93%. Features selected consistently across all individuals included volumes in the gyrus rectus, the right anterior lateral temporal lobe, the cuneus, and the third ventricle, as well as 2 clinical features: psychotic features and family history of mood disorder. CONCLUSIONS: Pretreatment structural MRI data improved the individual predictive accuracy of ECT remission, and only a small subset of features was important for prediction.

Evaluating the severity of depressive symptoms using upper body motion captured by RGB-depth sensors and machine learning in a clinical interview setting: A preliminary study.

BACKGROUND: Mood disorders have long been known to affect motor function. While methods to objectively assess such symptoms have been used in experiments, those same methods have not yet been applied in clinical practice because the methods are time-consuming, labor-intensive, or invasive. METHODS: We videotaped the upper body of each subject using a Red-Green-Blue-Depth (RGB-D) sensor during a clinical interview setting. We then examined the relationship between depressive symptoms and body motion by comparing the head motion of patients with major depressive disorders (MDD) and bipolar disorders (BD) to the motion of healthy controls (HC). Furthermore, we attempted to predict the severity of depressive symptoms by using machine learning. RESULTS: A total of 47 participants (HC, n = 16; MDD, n = 17; BD, n = 14) participated in the study, contributing to 144 data sets. It was found that patients with depression move significantly slower compared to HC in the 5th percentile and 50th percentile of motion speed. In addition, Hamilton Depression Rating Scale (HAMD)-17 scores correlated with 5th percentile, 50th percentile, and mean speed of motion. Moreover, using machine learning, the presence and/or severity of depressive symptoms based on HAMD-17 scores were distinguished by a kappa coefficient of 0.37 to 0.43. LIMITATIONS: Limitations include the small number of subjects, especially the number of severe cases and young people. CONCLUSIONS: The RGB-D sensor captured some differences in upper body motion between depressed patients and controls. If much larger samples are accumulated, machine learning may be useful in identifying objective measures for depression in the future.

Evaluating depression with multimodal wristband-type wearable device: screening and assessing patient severity utilizing machine-learning.

OBJECTIVE: We aimed to develop a machine learning algorithm to screen for depression and assess severity based on data from wearable devices. METHODS: We used a wearable device that calculates steps, energy expenditure, body movement, sleep time, heart rate, skin temperature, and ultraviolet light exposure. Depressed patients and healthy volunteers wore the device continuously for the study period. The modalities were compared hourly between patients and healthy volunteers. XGBoost was used to build machine learning models and 10-fold cross-validation was applied for the validation. RESULTS: Forty-five depressed patients and 41 healthy controls participated, creating a combined 5,250 days' worth of data. Heart rate, steps, and sleep were significantly different between patients and healthy volunteers in some comparisons. Similar differences were also observed longitudinally when patients' symptoms improved. Based on seven days' data, the model identified symptomatic patients with 0.76 accuracy and predicted Hamilton Depression Rating Scale-17 scores with a 0.61 correlation coefficient. Skin temperature, sleep time-related features, and the correlation of those modalities were the most significant features in machine learning. LIMITATIONS: The small number of subjects who participated in this study may have weakened the statistical significance of the study. There are differences in the demographic data among groups although we performed a correction for multiple comparisons. Validation in independent datasets was not performed, although 10-fold cross validation with the internal data was conducted. CONCLUSION: The results indicated that utilizing wearable devices and machine learning may be useful in identifying depression as well as assessing severity.