In total, 5397 singleton-term pregnancies were included; the prevalence of third-fourth-degree perineal tears was 0.47%. The most predictive factors were: nulliparity, feto-maternal BMI, neonatal weight, gestational age at delivery, and neonatal head circumference. After adjustment in multivariate analysis, the only independent predictors were nulliparity and fetomaternal BMI. The AUC of the final multivariate model was 73.54% (95% CI 65.65–81.42). Furthermore, feto-maternal BMI and gestational age had a significant direct correlation. Nulliparity and feto-maternal BMI are the two best predictors for third and fourth-degree perineal tears in our setting. Confirming this association in future research and integrating it into a decision algorithm on delivery timing could reduce obstetric damage to the anal sphincter.The main objective of this study was to evaluate the association between maternal and fetal anthropometric characteristics and third- and fourth-degree perineal tears. This retrospective cohort study considered all consecutive pregnancies from 2011 to 2017 at a single Institution. The inclusion criteria were: singletons who delivered vaginally during the study period, the presence of information on maternal pre-pregnancy weight, maternal height, and weight of the newborn. The feto-maternal body-mass index (BMI) was calculated as neonatal weight in kg on maternal height in squared meters (kg/m Perineal tears are injuries of the perineum related to childbirth involving the perineal soft tissues and muscles. They are defined as high-degree perineal tears if the anal sphincter complex is also concerned and are classified as third- and fourth-degree according to whether the anal epithelium is involved. High-degree perineal tears are not only major contributors to short-term morbidity due to local pain and wound breakdown but are also linked to subsequent chronic pelvic pain and dyspareunia, as well as a large spectrum of symptoms of loss of bowel control, including anal incontinence . A recenIn a previous study in our population, we observed an incidence of 1.3% of third- and fourth-degree perineal tears in a cohort of women who gave birth vaginally and were questioned about their quality of life one year after delivery . This prIn order to overcome this limitation, researchers argued that the feto-maternal body-mass index (BMI), the ratio between neonatal weight and maternal height, could be a good index for predicting obstetric damage to the anal sphincter. This new parameter was proposed for the first time at the International Urogynecological Association (IUGA) world congress in Toronto in 2010 as an ultrasound index for predicting the risk of damage to the anal sphincter during childbirth . HoweverThis retrospective cohort study considered all consecutive deliveries from 2011 to 2017 at the Obstetrics and Gynecology Unit of “Santa Maria Della Misericordia” University Hospital in Udine, Italy. The inclusion criteria were: singletons who delivered vaginally during the study period; the presence of information on maternal pre-pregnancy weight, maternal height, and weight of the newborn. Conversely, the exclusion criteria were childbirth by cesarean section, twin pregnancies, and the absence of neonatal and maternal anthropometric information.2).Maternal-fetal and neonatal data were routinely collected from the clinical database. The data considered were the following: maternal age, parity, maternal height, pre-pregnancy maternal weight, pre-pregnancy BMI, assisted reproductive techniques, and geographic origin. The pregnancy outcomes considered were: gestational age at delivery, mode of labor onset, delivery mode, presence of third- and fourth-degree perineal tears, hypertensive disorders of pregnancy (HDP), gestational age at birth, neonatal length, neonatal head circumference, placental weight, neonatal sex, neonatal weight, Apgar score at the 1st and 5th minutes, infant small for gestational age (SGA), large for gestational age (LGA), presence of neonatal congenital anomalies, neonatal resuscitation, and admission to neonatal intensive care unit (NICU). The feto-maternal BMI was calculated as neonatal weight in kg on maternal height in squared meters  for the prevention of obstetric anal sphincter injuries (OASIS) . Experiehttps://www.R-project.org/) [p < 0.05 were considered significant. Data are presented as the median and interquartile range (IQR) for non-parametric continuous variables; mean ± standard deviation in case of continuous parametric variables. Dichotomous variables are presented as percentage and absolute values, excluding missing values (NA). The results of the logistic regression models are presented as odds ratios (OR) and 95% confidence intervals (CI). The Kolmogorov–Smirnov test tested continuous variables’ distribution to establish the distribution’s normality. The following statistical tests were also used in the case of continuous variables: t-test for parametric variables and Wilcoxon test for non-parametric variables. Where appropriate, the Chi-square test or Fisher’s exact test was used for dichotomous variables. Finally, a logistic regression analysis was performed considering third- and fourth-degree lacerations as the dependent variable and possible risk factors as independent variables. The multivariate model evaluated all potential predictive factors with a p < 0.100 in the univariate analysis. All the variables and their interactions were entered into the initial multivariate model. In cases where the interactions were not significant, the analysis of the model without interaction was performed. The accuracy of the predictive models was assessed using the area under the curve (AUC) of the receiver operating characteristic (ROC) curves. Furthermore, the differences between the AUCs were evaluated by Delong’s test.Statistical analysis was performed using the R program ). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL ct.org/) . DiffereIn total, 5397 out of 13,349 deliveries of singleton pregnancies at term performed vaginally complied with the inclusion criteria A. The pr2 (IQR 1.12–1.34) and had a directly proportional and significant correlation with the gestational age at delivery . p < 0.05), had a higher feto-maternal BMI (p < 0.05), and had a higher gestational age at delivery (p = 0.071). p = 0.072) and a larger head circumference (p = 0.107). As expected, there was no association between SGA and third- or fourth-degree perineal injuries.p < 0.05), feto-maternal BMI (p < 0.05), gestational age at delivery (p = 0.079), neonatal weight (p < 0.05), or neonatal head circumference (p < 0.05). Univariate and multivariate logistic regressions were also performed, considering the most significant variables. In particular, the most predictive characteristics after a stepwise selection of the variables were found to be nulliparity  and feto-maternal BMI  . The mulThis retrospective study showed that feto-maternal BMI is significantly and independently associated with third- and fourth-degree perineal tears.In recent years, the feto-maternal BMI has emerged as a potential ultrasound index for predicting the risk of damage to the anal sphincter during childbirth . HoweverAccording to the literature, duration of second-stage labor, operative vacuum delivery, prior history of anal sphincter injury, maternal age, gestational age at delivery, and maternal race/ethnicity were associated with increased risk of injury to the anal sphincter ,22. In oAs highlighted in previous studies conducted in our obstetric population, third- and fourth-degree perineal lacerations with damage to the anal sphincter appear to be associated with major pelvic floor disorders even after the index delivery ,7,8. A rThis study thoroughly evaluates the feto-maternal BMI as a potential risk factor for high-degree perineal lacerations in a large cohort of women. However, the study’s retrospective design and a large amount of missing data weaken the strength of the results. Additionally, the study did not evaluate several known parameters associated with perineal tears, which further limits the reliability of the results. Our clinical policy recommends an elective cesarean section for women with a history of anal sphincter injury during previous childbirth, excluding multiparous women with a higher risk of experiencing vaginal lacerations during vaginal delivery. However, our results align with previous literature , and it Nulliparity and feto-maternal BMI are the two best predictors of third- and fourth-degree perineal lacerations in our setting. Integrating these risk factors into a decision algorithm for delivery management could potentially reduce obstetric damage to the anal sphincter.


Objectives: Red blood cell distribution width (RDW) is a widely used clinical parameter recently deployed in predicting various cancers. This study aimed to evaluate the prognostic value of RDW in patients with hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC).Methods: We conducted a retrospective study of 745 patients with HBV-related HCC, 253 patients with chronic hepatitis B (CHB), and 256 healthy individuals to compare their hematological parameters and analyze their RDW levels. Potential risk factors for long-term all-cause mortality in patients with HBV-related HCC were predicted using Multivariate Cox regression. A nomogram was generated, and its performance was evaluated.Results: The RDW of patients with HBV-related HCC was significantly higher than that of those with CHB and healthy controls. In the former, splenomegaly, liver cirrhosis, larger tumor diameter, multiple tumor number, portal vein tumor thrombus, and lymphatic or distant metastasis were significantly increased, and the later the Child-Pugh grade and Barcelona clinic liver cancer stage, the higher the RDW. Furthermore, multivariate Cox regression analysis identified RDW as an independent risk factor for predicting long-term all-cause mortality in patients with HBV-related HCC. Finally, we successfully generated a nomogram incorporating RDW and validated its predictive ability.Conclusions: RDW is a potentially valuable hematological marker for predicting the survival and prognosis of patients with HBV-related HCC. The nomogram incorporating RDW can be used as an effective tool to plan the individualized treatment of such patients. According to the Global Cancer Statistics 2020 study, primary liver cancer had an estimated 906,000 new cases and 830,000 deaths, making it the sixth most diagnosed cancer and the third most common cause of cancer mortality globally. Hepatocellular carcinoma (HCC) 75%-85%), intrahepatic cholangiocarcinoma 10%-15%), and other uncommon forms are among the primary liver cancers 0%-15%, a%-85%, inA hematological characteristic called red blood cell distribution width (RDW), which indicates the heterogeneity of red blood cell volume, is frequently employed in the differential diagnosis of anemia Smirne et al. The cases of 745 patients with HBV-related HCC and 253 with chronic hepatitis B (CHB) who were first diagnosed at the 900TH Hospital of Joint Logistics Support Force (from January 2016 to December 2021) were evaluated in this study.The following criteria were used to select patients with HBV-related HCC: (1) computed tomography (CT)/magnetic resonance imaging (MRI) or pathological diagnosis of HCC, (2) hepatitis B surface antigen positivity for >6 months, (3) diagnosis for the first time and no prior antitumor therapy, and (4) complete clinical and follow-up records. In addition, the exclusion criteria included the following: (1) superinfection with other hepatitis viruses; (2) combination with other etiologies, such as the use of alcohol or drugs or autoimmune hepatitis; (3) history of other cancers; (4) gastrointestinal bleeding over the past 6 months; (5) combination with other hematological diseases; and (6) combination with pregnancy or severe immune system deficiency.The following criteria were used to select patients with CHB: (1) diagnosis per Chinese recommendations for the treatment and prevention of CHB (2019 version) Additionally, 256 individuals without a history of cardiovascular or hematological illnesses and with normal liver and kidney function were enlisted as controls.Ethical approval was obtained from the 900TH Hospital of Joint Logistics Support Force . The need for informed consent was waived for this investigation because all patient data were de-identified and anonymized before analysis.We gathered baseline demographic information , hematological parameters , and tumor information . In addition, we recorded whether patients with HBV-related HCC had enlarged spleens or hepatocirrhosis. The Child-Pugh grade was calculated to describe liver function. Tumor staging at diagnosis used the BCLC criteria and was classified into five categories . Prior to antitumor treatment, each indicator, including RDW, was detected.In patients with HBV-related HCC, the diagnosis date was the date of the radiological diagnosis, not the date of the subsequent pathological confirmation. OS was also described as the period from the radiological diagnosis to the last follow-up or death resulting from any reason. Professional radiologists evaluated diagnostic CT scans or MRI results to determine the radiological tumor characteristics in patients with HBV-related HCC.p < 0.05 were finally included into the model. Next, all patients with HBV-related HCC were randomly assigned to a training cohort (80%) or a validation cohort (20%) using R software. Subsequently, we developed a nomogram to determine the prognosis of patients with HBV-related HCC. The performance of the nomogram was evaluated through internal validation and calibration curves. After that, we assessed the nomogram's clinical value using the decision curve analysis (DCA). Finally, a total risk score (NomoScore: nomogram risk score) was calculated for each patient based on the nomogram models, and the cut-off point was automatically calculated using the X-tile software  to categorize the patients into the following three risk groups: high-, moderate-, and low-risk.Quantitative variables with non-normal distributions were presented as medians with interquartile ranges (25th-75th percentiles), whereas categorical variables were presented as absolute counts and percentages. A two-tailed chi-squared or Fisher's exact test was used to compare categorical variables. The Mann-Whitney rank-sum or Wilcoxon rank-sum test was used to compare non-normal continuous variables. Survival curves were calculated using the Kaplan-Meier method and compared using the log-rank test. Multiple Cox regression analysis with a forward likelihood ratio method was conducted to analyze the effect of multiple covariates on survival. In this study, we incorporated eight continuous variables  and nine categorical variables , and only the variables having a p < 0.05.The R software 4.1.3  and SPSS 26.0  were used for statistical analysis, and Adobe Illustrator  and GraphPad Prime 9.0  were used for plotting. Statistics were deemed to be significant for any value with p values < 0.05.Table  p values < 0.001.We subsequently assessed the differences in RDW levels in each subgroup in the total cohort of patients with HBV-related HCC, as reported in p < 0.05.Eight variables persisted in the model after the Cox proportional hazard regression models were utilized to determine independent prognostic factors for OS . The resp > 0.05, The HBV-related HCC cohort was randomly divided into training and validation groups to develop a nomogram for individualized clinical treatment. The two groups of patients were not significantly different in terms of diverse basic variables, such as age, RDW, Child-Pugh grade, maximum tumor diameter, tumor number, PVTT, distant metastasis, and BCLC stage .p < 0.05, Figure. 5).The total risk NomoScores were calculated for each patient according to the nomogram model, and the patients with HBV-related HCC were categorized into different risk groups using the optimal cut-off points to further judge the capability of the nomogram to discriminate the survival. In the OS group, the total risk NomoScores of > 208 was the high-risk group, < 151 was the low-risk group, and those in between were the moderate-risk group. In the 1-, 3-, and 5-year OS groups, the high-risk group was defined as a total risk NomoScores > 208, the low-risk < 142, and the rest fell into the moderate-risk group. Finally, the survival curves were calculated using the Kaplan-Meier method, and the OS, 1-, 3-, and 5-year OS of the three different risk groups were compared. The results showed that risk grouping based on the NomoScores had a significant value in identifying mortality . When ROS accumulation is out of control, red blood cells tend to be destroyed, and their lifespan is shortened Inflammation, malnutrition, and oxidative stress are examples of putative pathways that could explain the link between RDW and a poor prognosis in patients with HBV-related HCC. First, inflammation is one of the major underlying factors in the onset and progression of cancer In our series of studies, we discovered that individuals with HBV-related HCC who had elevated RDW had a poor prognosis. Therefore, we suggested that RDW might be used to improve these patients' prognoses. Nevertheless, this study bears some limitations. First, there may have been selection bias due to its retrospective nature. Second, the effect of age on RDW cannot be completely denied. Third, the variables included in the study were insufficiently broad; for instance, the lack of clinical data availability prevented the inclusion of HBV-DNA. Finally, we did not dynamically monitor changes but merely used a single pretreatment RDW value as a prognostic predictor. Therefore, high-quality prospective randomized controlled trials are needed to confirm the prognostic significance of RDW in patients with HBV-related HCC.RDW, a simple and widely used hematological parameter, is significantly increased in patients with HBV-related HCC and is associated with poor prognosis, which may be utilized as a potential prognostic indicator for those patients. In addition, the nomogram generated by incorporating RDW has high accuracy in predicting the survival and prognosis of patients with HBV-related HCC and can be used as an effective tool for individualized clinical treatment and prognosis judgment.Supplementary figures and tables.Click here for additional data file.


Isopropyl alcohol molecules, as a biomarker for anti-virus diagnosis, play a significant role in the area of environmental safety and healthcare relating volatile organic compounds. However, conventional gas molecule detection exhibits dramatic drawbacks, like the strict working conditions of ion mobility methodology and weak light-matter interaction of mid-infrared spectroscopy, yielding limited response of targeted molecules. We propose a synergistic methodology of artificial intelligence-enhanced ion mobility and mid-infrared spectroscopy, leveraging the complementary features from the sensing signal in different dimensions to reach superior accuracy for isopropyl alcohol identification. We pull in “cold” plasma discharge from triboelectric generator which improves the mid-infrared spectroscopic response of isopropyl alcohol with good regression prediction. Moreover, this synergistic methodology achieves ~99.08% accuracy for a precise gas concentration prediction, even with interferences of different carbon-based gases. The synergistic methodology of artificial intelligence-enhanced system creates mechanism of accurate gas sensing for mixture and regression prediction in healthcare. Isopropyl alcohol can play a significant role in anti-virus diagnosis but generally yields limited response in conventional detection. Here, authors propose a methodology of AI-enhanced ion mobility and mid-infrared spectroscopy for accurate gas identification despite presence different carbon-based gases. Many virus-perishing methods are investigated for combatting pandemic or cold diseases, such as managing indoor air, increasing gas ventilation, optimizing humidity levels, maximizing air filtration efficiency, and the widespread use of IPA. IPA is a flammable and colorless liquid with a fruity odor and a slightly bitter taste. It is used to clean electronic components and to remove the thermal paste from integrated circuit chip packages. Due to its extensive use, various healthcare problems may occur along with the exposure or inhalation of the IPA molecules, e.g., skin irritation, nervous system illness, and respiratory damage. As a result, rapid and accurate detection of IPA molecules becomes critical. The methods for IPA sensing include chemiresistive, gas absorption-based, two-dimensional(2-D) material-based, ion mobility, electrochemical, and other sensors16. However, such IPA sensors can only measure the total gas concentration among various volatile organic compounds(VOCs), and they have limitations such as slow response, poor selectivity, and insufficient accuracy. Thus, a sensing system with fast response, good selectivity, anti-interference, and high sensitivity for IPA identification is critical.The need for health and safety has been increasing the demand for isopropyl alcohol (IPA) as a vital element of anti-virus hand sanitizers23. Mid-infrared spectroscopy is a non-destructive method applied to identify the vibrational modes of gas molecules, and it has widespread applications in environmental monitoring, pharmacy, chemical analysis, bio-sensing, and the pharmaceutical industry. Regarding the sensitivity enhancement in mid-infrared spectroscopy, it is found that the instantaneous strong electrostatic field could be further tailored to the molecular vibration along with the optics refection response, thereby enhancing the absorption effect of infrared spectroscopy. In short, conventional ion mobility sensing has the constraint of the requirement of a strict measurement environment, whereas conventional mid-infrared spectroscopy is limited by poor response during low-concentration identification. Overall, both methods have drawbacks in IPA sensing at this point.Ion mobility and mid-infrared spectroscopy are two well-known analytical techniques for identifying of gas-phase compounds. Scientists focus on ion mobility and investigated many approaches to increase sensing ability, e.g., producing ions by β-radiation, ultraviolet (UV) irradiation, and others. However, this approach requires a high-voltage source and a strict operating environment , yielding expensive and bulky equipment33 was discovered in 2012, allowing for an additional high-voltage output from mechanical vibrations. With the aid of triboelectric, Wang et al. reported respiration health based on AI-assisted diagnosis by multi-self-calibrated parameters from TENG with accuracy >95.21%33. However, anti-virus VOCs diagnosis from hybrid mechanism (mid-infrared spectroscopy) and data-enhanced algorithms still need to be investigated. For example, the ion power source from the triboelectric nanogenerator could be greatly optimized and provide ion mobility in cold environments and ambient air pressure. Thirdly, artificial intelligence (AI) is a hot research topic dramatically promoted with the fifth-generation cellular network technology (5 G). A cloud server would easily handle plenty of data based on deep learning algorithms in a short time39. Providing feasible big data processing using machine learning and deep learning techniques (t-distributed stochastic neighbor embedding (t-SNE), linear discriminant analysis (LDA), principal component analysis (PCA), synthetic minority oversampling technique (SMOTE), and deep neural networks (DNN) regression), which may reinforce the characteristic IPA data-sets from ion mobility and mid-infrared. Thus, the AI-enhanced methodology would be an ideal solution for IPA detection in factories or personal healthcare, where identifying IPA molecules requires rapid response, high accuracy, good selectivity, anti-interference, and high sensitivity.Herein, three critical issues need to be addressed in order to improve the response of IPA molecules using existing ion mobility or mid-infrared spectroscopy. Firstly, focusing on the interaction of molecules with infrared irradiation, high-voltage plasma induces a strong electromagnetic field, yielding a coupling effect, which amplifies the mid-infrared absorption response. Therefore, plasma-enhanced infrared absorption spectroscopy enables the ultrasensitive detection of molecules with the aid of an existing high-voltage power source (ion mobility). Secondly, regarding ion mobility, a triboelectric nanogeneratorR2 score by DNN regression, which can be improved to ~0.98 R2 score using SMOTE before DNN regression. What’s more, it is also demonstrated that the data preprocessing method could remove the background calibration with high accuracy in feature classification. In the perspective of sensing fusion between IMMS, the regression performance in concentration prediction can be enhanced to a ~0.99 R2 score. Moreover, even in the case of different gas interferences, this synergistic methodology of IMMS reaches ~99.08% accuracy in our study using the AI-enhanced method and high-voltage triboelectric cold ion mobility.To realize the IPA detection with good selectivity, fast response, and high sensitivity, we propose an artificial intelligence (AI)-enhanced chemical detection based on IMMS assisted by a multi-switched triboelectric nanogenerator. It provides an additional high-voltage power source for ion mobility and plasma enhancement for mid-infrared sensing. The plasma-enhanced mid-infrared response in IPA sensing exhibits a good concentration prediction with ~0.87 Voc) and the accumulative open-circuit charges (Qoc) are presented in Figs. SAs shown in Fig. 37. Thus, the AI-enhanced approach can process multi-modal data from the collected data, and it can be further improved with the synergistic methodology to realize the fast response, wide detection range, and accurate detection of a gas mixture.The IPA response mechanism is from the characterizations in absorption and reflection by mid-IR. The external-provided strong electric field dramatically improves the sensitivity of the IPA detection due to the high-voltage fluctuation from the plasma discharge onto the vibrational mode of the molecule structure (coupling effect). Thus, the synergistic mechanism of ion-mobility sensing and enhanced-mid-IR reflection enables chemical sensing to achieve fast response times and accurate detection (two kinds of data in computing). Furthermore, the data processing with an AI-enhanced approach would combine the advantage of both ion-mobility sensing and enhanced-mid-IR response resulting in extra high accuracy and wide-range detection. The detailed experiment photos of the AI-enhanced IMMS can be found in Fig. SThe ion mobility system is constructed in a tip-plate electrode configuration, high-voltage component, and current collector, as shown in Fig. R2 score is as follows:In order to enhance the accuracy of IPA detection, we adopted raw data analysis both with and without SMOTE + DNN regression for an in-depth study of the dark current pattern, as illustrated in Fig. R2 score for the test dataset was 0.96, which represents a good regression performance.Here the −1, characteristic for CO2, a ~1–3 times higher response is observed than without plasma . The AI-enhanced methodology for mid-infrared spectroscopy is shown in Fig. −1~3500 cm−1. The relationship between different IPA concentrations with almost a linear numerical relationship is shown in Fig. S42. To accurately evaluate the noise in real measurement, the 0 ppm IPA molecules are chosen to extract the signal fluctuations of these spectra. By plotting the total noise and the output signal with SMOTE + DNN regression together, it can analyze the LOD of the Ai-enhanced IMMS. The SMOTE + DNN regression of the signal above the noise at 0 ppm indicates that the LOD of our IPA could reach a prediction of ~40 ppm based on the estimated mean error using an improved IMMS technique . However, the manual calibration before the real measurement is inconvenient and inefficient. To avoid this manual process (labor-consuming), we adopt an artificial intelligent method to provide the right concentration measurement. The raw data without data preprocessing is shown in Fig. 38 and a decision tree. As shown in Fig. Figure 45. To further demonstrate the IPA detection in mixture gases, as shown in Fig. −1, clearly implying that the peak response of different gas species can be well-identified. The gas species are determined by the response of molecules with their mode at a certain wavenumber, whereas their concentrations are indicated by the response value. IPA mixture gas detection based on the plasma-enhanced mid-infrared spectrum is represented in Fig. SR2 score, which is improved compared with a single measurement as illustrated in Fig. IPA as one of the important elements in VOCs family can affect human health and be used for the assessment of various diseases. For example, IPA has been widely used as antiviral hand sanitizer. Also, many studies have indicated that IPA levels can reflect the severity of lung cancer, serving as a specific biomarker for early diagnosis, which deserves attention and further researchIn conclusion, we propose a synergistic IMMS mechanism for AI-enhanced chemical sensing to achieve rapid response and accurate detection of a gas mixture. A triboelectric nanogenerator for high-voltage plasma is proposed to overcome the environmental pressure constraints to ion mobility and the weak response and reflection detection of gas molecules, limiting the use of mid-infrared spectroscopy. The reported self-powered ion mobility reaches up to almost two times higher accuracy with the aid of AI-augmentation by automatic extracting of the specific features than the conventional method. It is also shown that cold plasma from the triboelectric generator enhances the mid-infrared response for IPA sensing with a good linear prediction by deep learning. Moreover, the data processing could remove the background calibration based on our observation with a labor-consuming effect. Due to the challenges in IPA detection, the AI-enhanced approach is successfully demonstrated by extracting the features from the IMMS data with ~99.08% accuracy. This method can process multi-modal data from collected data, and can be combined with existing methodology to realize the synergistic mechanism.2. For power output, a positive triboelectric polymethyl methacrylate (PMMA) dielectric is connected with negative fluorinated ethylene propylene (FEP). The laser cutting machine is used to cut the acrylic plate for component assembly. The conductive nickel textile is used to connect and separate with/from an electrode via the action of “on” or “off”.The multi-switched triboelectric nanogenerator contains the bottom pair of plates and a top movable pair of plates with a contact area of 49 cmVoc, and Qoc parameters. The infrared spectra are recorded using an FTIR spectrometer (Agilent Cary 610 Series), which operates at wavelengths from 4 to 8 μm. The measurement environment is at room temperature, exhibiting the tolerance of the test environment. The calibration sensor of the IPA is a commercial device.A programmable electrometer (Keithley model 6514) is adopted for The platform is made of the multi-switched triboelectric nanogenerator, the pre-mixture IPA chamber, and the FTIR equipment. The pre-mixed chamber is constructed to mix different gases. The calibration sensor (SKY2000-VOC) is used to identify the specific concentration of IPA. An oscilloscope is used to monitor the dark current from the collection electrode to record the dark current response along with the gases. A computer is used to record FTIR data for further machine learning. The gas chamber is sealed during the operation to improve the accuracy of the IPA concentration measurement. The oscilloscope is connected to the plate electrode collector to record the dark current response during the sliding of the multi-switched triboelectric nanogenerator.Synthetic minority oversampling technique is a synthetic data algorithm with comprehensive sampling, which can be used to solve the problem of unbalanced data distribution. Owing to the limitation of data collection, it is difficult to ensure that the information of sampled IPA data in different concentrations is distributed evenly, yielding worse performance in the predictive model. Thus, SMOTE which realizes in MATLAB R2022a is adopted for each concentration of IPA data. The data of each concentration should be equal in amount so that it can be used in the classification or regression model. The deep neural network is developed for the gas feature regression and IPA concentration estimation. The proposed DNN is developed on python 3.8 using the Keras and sci-kit-learn packages. Our DNN model is specifically constructed by multiple connected layers: dense  → dropout (0.2) → dense  → dense . The batch size is 20. The epoch number is 300. The loss function between the predict value and the true value is a mean-squared error. The optimizer is Adam. The ratio settings for train/test split are 70:30, 75:25, and 80:20. The result with different ratio settings are shown in Figs. SThe t-SNE is used in exploratory data analysis and for nonlinear dimensionality reduction. t-SNE reserves the local characteristics by transforming the data distance relationship into a probability distribution. When implementing t-SNE, the same amount of low-dimensional data is randomly generated, and then the loss function measures the difference between two probability distributions. The gradient descent method is used to update this batch of data, and the low-dimensional data satisfying the requirements is obtained. The t-SNE is performed in MATLAB R2022a to intuitively visualize the feature space in two and three dimensions. The support vector machines by the “libsvm” package in MATLAB 2022a are implemented to compare with LDA.Further information on research design is available in the Supplementary InformationReporting Summary


Cells are the most basic structural and functional units of living organisms. Studies of cell growth, differentiation, apoptosis, and cell-cell interactions can help scientists understand the mysteries of living systems. However, there is considerable heterogeneity among cells. Great differences between individuals can be found even within the same cell cluster. Cell heterogeneity can only be clearly expressed and distinguished at the level of single cells. The development of droplet microfluidics technology opens up a new chapter for single-cell analysis. Microfluidic chips can produce many nanoscale monodisperse droplets, which can be used as small isolated micro-laboratories for various high-throughput, precise single-cell analyses. Moreover, gel droplets with good biocompatibility can be used in single-cell cultures and coupled with biomolecules for various downstream analyses of cellular metabolites. The droplets are also maneuverable; through physical and chemical forces, droplets can be divided, fused, and sorted to realize single-cell screening and other related studies. This review describes the channel design, droplet generation, and control technology of droplet microfluidics and gives a detailed overview of the application of droplet microfluidics in single-cell culture, single-cell screening, single-cell detection, and other aspects. Moreover, we provide a recent review of the application of droplet microfluidics in tumor single-cell immunoassays, describe in detail the advantages of microfluidics in tumor research, and predict the development of droplet microfluidics at the single-cell level. The metagenomic screening system identified six unique sulfatase variants and eight unique phosphotriesterases. Droplet microfluidics can also screen genetically engineered bacteria, such as genetically modified cyanobacteria and wild-type ethanol-producing and lactic acid-producing strains. Abalde and Hammar’s team cultured the cells in droplets and made the bacteria secrete ethanol or lactic acid  changes in cellular metabolites. Due to increased glycolysis, tumor cells had a higher free/bound NADH ratio than normal cells. Another important application of single-cell assays is targeted and genome-wide analysis at the single-cell level and quantifying single-cell protein levels . GenomicPreviously, single cell DNA analysis was most commonly performed with laboratory-developed approaches relying upon FACS sorting to first isolate single-cells in 96- or 384-well plates. Not only are these approaches laborious and slow, but they also utilize significant amounts of reagent to generate sequence information. The droplet microfluidic method can generate sequence libraries in a short time and, through the use of picoliter volume droplets, consumes minimal reagent to barcode genomic DNA. These key features significantly lower the barriers for performing single-cell DNA sequencing and promise to make high-resolution analysis of clonal architecture within tumors routine. Mission Bio  developeDroplet microfluidic not only has broad application prospect in single cell targeted genome analysis, but also plays an important role in whole genome analysis. Single-cell DNA sequencing (scDNA-Seq) can identify somatic genetic changes, such as somatic cell copy number variation (CNVs). For cancer, single-cell CNVs characterize high resolution intratumoral heterogeneity and subclonal structures present in primary tumors, metastases, patient-derived xenografts, and even cancer cell lines. However, the cellular throughput of scDNA-Seq has been limited, with a typical maximum of hundred cells. Greater sampling of tumor tissues provides an opportunity to expand the scope of intratumoral characterization. As a solution that enables massive scale scDNA-Seq, The human body’s immune system is a large and complex reaction system that can cope with and recognize many environmental factors and respond to various signals . Differe+ T helper cells based on a single-cell transcriptome; they were able to further identify multiple T helper cell subpopulations with different functions to the two identified subpopulations, Th1 and Th2.Single-cell technology involves isolating individual cells from a population to extract data from each cell and obtain cell-related information for analysis. After isolating a single immune cell, multiple experimental operations of DNA and RNA sequencing and protein expression profiles can be performed to map the immune cell lineage and identify immune cell subsets . RutkausIn immunology research, flow cytometry is a recognized single-cell analysis tool that can perform high-throughput analysis of thousands of single cells in a reasonable time frame and simultaneously measure multiple parameters . HoweverMicrofluidic devices have also isolated circulating tumor cells from clinical samples. Circulating tumor cells contain all tumor-related genes. Knowledge of genetic changes in circulating tumor cells can help select appropriate therapies for different patients, which can be used for diagnosis, prognosis, and creation of patient-derived tumor models . High-thGel droplets also have many advantages over water-based droplets in single-cell analysis. Huck’s lab used agarose droplets to encapsulate Jurkat T cells . As showIn addition, droplet microfluidics can also facilitate the manipulation of cells by manipulating droplets. in vivo (In the past decade, microfluidics technology has greatly improved our understanding of immunology and the study of tumor cells. The technology also provides a unique opportunity to develop new therapeutics and oncology drugs. The introduction of microfluidics made it possible to study T-cell activation at the single-cell level and even brought new insights into the pairing of individual immune cells and cancer cells . Based oin vivo . In addiDroplet microfluidic technology has developed rapidly in the past decade and has solved many problems in biological research. In particular, single-cell technology is the most striking. As shown in However, there are challenges associated with research on droplet microfluidics and single-cell analysis, and there are still many problems to be overcome. Most current methods are limited to a single type of information output, such as data related to DNA, RNA, or protein. Quantifying multiple layers of information simultaneously at a single cell level is a current goal of scientists in the field. Another major challenge of the single-cell approach is the lack of information on dynamic processes, further complicated by differences in cell heterogeneity in samples. Moreover, tissue separation is required before single-cell encapsulation, which inevitably leads to the loss of spatial information about the original location of cells in the tissue. Research into the spatial transcriptome seeks to combine cellular transcriptome information with spatial organization within tissues using computational techniques to obtain missing information in both cases . The comEven so, droplet microfluidic diagnostics also have promising applications. The simplicity and cost-effectiveness of microfluidic diagnostics will enable single-cell research to be applied in biomedicine, chemical systems, and clinical biology.


COVID-19 has had a disproportionate impact on the most disadvantaged members of society, including minorities and those with disabling chronic illnesses such as schizophrenia. We examined the pandemic’s impacts among New York State’s Medicaid beneficiaries with schizophrenia in the immediate post-pandemic surge period, with a focus on equity of access to critical healthcare. We compared changes in utilization of key behavioral health outpatient services and inpatient services for life-threatening conditions between the pre-pandemic and surge periods for White and non-White beneficiaries. We found racial and ethnic differences across all outcomes, with most differences stable over time. The exception was pneumonia admissions—while no differences existed in the pre-pandemic period, Black and Latinx beneficiaries were less likely than Whites to be hospitalized in the surge period despite minorities’ heavier COVID-19 disease burden. The emergence of racial and ethnic differences in access to scarce life-preserving healthcare may hold lessons for future crises.The online version contains supplementary material available at 10.1007/s10597-023-01105-1. COVID-19 has had a disproportionate morbidity and mortality impact on the most disadvantaged members of society, including racial and ethnic minorities and those with disabling chronic illnesses including serious mental illnesses  in any position. We created an open cohort, meaning beneficiaries could enter or exit anytime between 1 year preceding the onset of the COVID-19 pandemic in the state , inclusive of a pneumonia season , and a 70-day early phase following its onset . We selected March 7, 2020 as the date of pandemic onset because the state declared a state-of-emergency to contain the spread of the virus on this date  antipsychotic drug prescription fills, both of which individuals receiving care for schizophrenia should access regularly, and (3) high-intensity BHO care, a composite measure that captured monthly utilization of any of three team-based specialty services typically reserved for more seriously ill patients: Assertive Community Treatment (ACT); Personalized Recovery Oriented Services (PROS), a psychosocial rehabilitation program; or behavioral health Home and Community-Based Services. The other three measures captured inpatient utilization for life-threatening emergencies: (4) psychiatric admissions for suicidality ; (5) admissions for cardiometabolic emergencies , a composite measure that captured admissions for cardiovascular disorders or diabetes mellitus; and (6) pneumonia admissions assessed during the pneumonia season, which as of March 2020, included COVID-19 pneumonia.We constructed six binary outcome measures, all but one assessed daily, selected based on their critical importance for this population. Individuals receiving care for schizophrenia need close outpatient monitoring of symptoms and treatment response, often need high-intensity community-based services, have a high suicide risk, and are also at high risk for serious medical comorbidities. Three measures captured utilization of BHO care: (1) Our main independent variables of interest were beneficiary race and ethnicity , time (count of days or months set to 0 at the initiation of the pandemic), pandemic period (pre- and post-pandemic), and their interaction. Other variables included beneficiary sex, age , county of residence (N = 62), and whether Medicaid-eligible through Supplemental Security Income (SSI), which we employed as a health status indicator given that SSI eligibility requires demonstration of disability. See Supplementary Section “Analytic Plan” for further details on our outcome and independent variables.Standardized mean differences of person and person-day characteristics between the post and pre-pandemic periods using the standard deviation in the pre-period were computed to assess balance. Standardized differences larger than 0.10 or smaller than − 0.10 suggest imbalances between the two periods. We estimated unadjusted rates of service utilization expressed as mean and standard errors (SEs) per 100,000 person-days or, for high-intensity BHO care, person-months. We determined the mean post-pandemic versus pre-pandemic differences (post–pre-pandemic differences) and 95% confidence intervals (CIs), and for the two composite measures , we also provide results for the individual components.For adjusted analyses, we utilized logistic regression models at the beneficiary-day (month) level, modeling the probability of occurrence of the outcome on the day (month), and estimated separate models for each of the six outcomes. All models included race and ethnicity (White as reference), time (day or month), sex , SSI, age groups (45–64 years as reference), pandemic period (pre-period as reference), and county fixed effects (New York City as reference), with possible pairwise interaction terms including race and ethnicity, time, and county. Day (or month) was parameterized using linear and quadratic terms to capture non-linear trends. Models that included interactions of race and ethnicity with day (month), pandemic period, and county of residence were also estimated . A final model for each outcome was selected using the model with the smallest Akaike Information Criterion . Due to the different durations of the pre- and post-pandemic periods, these individuals contributed different numbers of person-days, 22,976,921 and 4,383,072, respectively  who were excluded due to not having county information tended to be older, more male, and Black, and less likely to be White or have SSI (see Supplementary Table 2 showing characteristics of beneficiaries included compared to those excluded).Differences in utilization rates between the pre- and post-pandemic periods were only observed for high-intensity BHO care and pneumonia admissions. While rates increased for both outcomes, as expected, the increase for pneumonia admissions was large: pre-pandemic mean (SE) was 3.3 (0.3) per day and a mean (95% CI) post–pre-pandemic difference of 12.2  per day (see Supplementary Table 3 showing unadjusted utilization rates).antipsychotic drug prescription fills, and suicide-related admissions . On the other hand, a pre-pandemic difference for those of Unknown race and ethnicity dissipated, and it became smaller for Asian/Other beneficiaries .Our study of the early access effects of the COVID-19 pandemic among adult Medicaid beneficiaries with schizophrenia in New York State found that utilization of critical services and differences by race and ethnicity remained largely unchanged following the onset of the pandemic. This constancy is noteworthy given the severity of the pandemic in the state and how much it strained its healthcare system. However, like others Below is the link to the electronic supplementary material.


The user-friendly design allows easy access to compare the influence of sample preparation procedures, combinations of liquid chromatography (LC)-MS setups, as well as intra- and inter-software differences on critical performance measures across an unlimited number of analyses. mpwR supports outputs of commonly used software for bottom-up proteomics, such as ProteomeDiscoverer, Spectronaut, MaxQuant, and DIA-NN.https://CRAN.R-project.org/package=mpwR) for all major operating systems. The development version is maintained on GitHub (https://github.com/okdll/mpwR) and full documentation with examples and workflow templates is provided via the package website (https://okdll.github.io/mpwR/).mpwR is available as an open-source R package. Release versions can be accessed on CRAN ( The field of proteomics advances rapidly driven by the great potential of the underlying biological data. The promise of new insights continuously motivates to develop new sample preparation techniques, improved liquid chromatography (LC)-MS setups, as well as advanced data analysis solutions. The infinite process of improving individual parts of this proteomic equation requires researchers to constantly evaluate established workflows against recent progresses. Consequently, there is a need for a standardized analysis pipeline which empowers researchers to comprehensively monitor enhancements in a fast-paced proteomic field. While existing bottom-up proteomics R packages, such as protti  or data-independent acquisition (DIA). mpwR offers several useful functions to track crucial performance measures, such as number of identifications, data completeness, number of missed cleavages, as well as quantitative and retention time precision. This comprehensive overview is especially beneficial to emphasize common tendencies across various experiments or large-scale comparisons. As an example, mpwR provides a valuable framework for a reproducible analysis of round robin studies. Furthermore, since there is a variety of proteomic software used in the community for analyzing DDA and DIA data and since the software itself is an ever-improving variable, mpwR is compatible with common software, such as MaxQuant , ProteommpwR facilitates the comparison of proteomic workflows with a wide array of functions by analyzing important performance characteristics including number of identifications on precursor, peptide, protein and protein group level, data completeness, number of missed cleavages, as well as quantitative and retention time precision .For comparing proteomic workflows, the user can submit multiple analyses with specific adjustments to mpwR. Each analysis needs to be performed individually with two or more replicate measurements followed by a designated software analysis resulting in an output per analysis . Note, thttps://CRAN.R-project.org/package=flowTraceR) and the development version on GitHub (https://github.com/okdll/flowTraceR).Furthermore, functions from flowTraceR, an R package which creates standardized output formats on precursor, peptide, and protein group level for MaxQuant, Proteome Discoverer, Spectronaut, and DIA-NN is incorporated in the analysis of mpwR. This combination enables researchers to perform and evaluate inter-software comparisons. The release version is available on CRAN (https://okdll.github.io/mpwR/). Also, the modularized structure of mpwR allows easy workflow adjustments for a user-tailored analysis.mpwR supports the comparative analysis of bottom-up label-free proteomics DDA and/or DIA data from MaxQuant, Proteome Discoverer, Spectronaut, and DIA-NN. For R novices a low-level entry is provided via RMarkdown templates with thorough documentation for an mpwR data analysis pipeline for each software and for inter-software comparisons (https://okdll.shinyapps.io/mpwR/). The developed dashboard allows users to quickly analyze data without any R knowledge.In addition, each core functions and visualizations are integrable into the R shiny framework for custom-made dashboards or apps. As an example, a shiny dashboard was designed for small scale comparisons (Advanced R users can expand mpwRs functionalities to other proteomic software tools if the required information is accessible, e.g. precursor, peptide, and protein group information. A template for a generic input is provided. Moreover, users can add self-made modules about sequence coverage, charge state, or other characteristics of interest to the existing pipeline.mpwR offers a systematic approach for comparing proteomic workflows and empowers researchers to access valuable information about identifications, data completeness, quantitative precision, and other performance indicators across an unlimited number of analyses and multiple software tools. The resulting comprehensive overview provides a sound fundament to evaluate potential proteomic advances and promises quick data analysis turnaround times in the process of workflow optimization.


Sleep disturbances and excessive daytime sleepiness (EDS) are common non-motor symptoms in patients with Parkinson's disease (PD). The purpose of this study was to identify the contributors to sleep disturbances, including insomnia, restless legs syndrome, rapid eye movement sleep behavior disorder (RBD), sleep-disordered breathing, nocturnal akinesia and EDS, in patients with PD.We conducted a cross-sectional study including 128 consecutive Japanese patients with PD. Sleep disturbances and EDS were defined as a PD Sleep Scale-2 (PDSS-2) total score ≥15 and an Epworth Sleepiness Scale (ESS) score >10, respectively. The patients were divided into four groups according to the presence or absence of sleep disturbances and EDS. We evaluated the disease severity, motor symptoms, cognition, olfactory test, the Scales for Outcomes in PD-Autonomic dysfunction (SCOPA-AUT), the Beck Depression Inventory-II (BDI-II), and the RBD Screening Questionnaire Japanese version (RBDSQ-J).P = 0.002) or EDS  and that the BDI-II  and RBDSQ-J scores  as well as the SCOPA-AUT score  were independent contributors to both sleep disturbances and EDS.Of 128 patients, 64 had neither EDS nor sleep disturbances, 29 had sleep disturbances without EDS, 14 had EDS without sleep disturbances, and 21 had both EDS and sleep disturbances. Patients with sleep disturbances had higher BDI-II scores than those without sleep disturbances. Probable RBD was more frequent in patients with both sleep disturbances and EDS than in those with neither EDS nor sleep disturbances. The SCOPA-AUT score was lower in patients with neither EDS nor sleep disturbances than in patients in the other three groups. Using multivariable logistic regression analysis with neither sleep disturbances nor EDS as a reference group, that the SCOPA-AUT score was an independent contributor to sleep disturbances (adjusted OR, 1.192; 95% CI, 1.065–1.333; Autonomic symptoms were associated with patients with sleep disturbances or EDS, and depressive and RBD symptoms in addition to autonomic symptoms were associated with patients with both sleep disturbances and EDS. Parkinson's disease (PD) is the most common neurodegenerative movement disorder  and is aThis single-center, cross-sectional study was performed in accordance with the Declaration of Helsinki guidelines and approved by the Institutional Review Board of Dokkyo Medical University. All participants provided written informed consent.One hundred and twenty-eight consecutive Japanese patients with PD who visited the Department of Neurology, Dokkyo Medical University, between April 2018 and March 2022 were included. Bedridden patients who were unable to answer the questionnaire or who regularly used hypnotics or other sleep-promoting medications (such as antidepressants) were excluded from this study. Information on medical history that could potentially influence sleep conditions was collected from medical records. The diagnosis of PD was made based on the International Parkinson and Movement Disorder Society's published clinical diagnostic criteria .In the baseline assessment, disease severity was rated according to the Hoehn and Yahr (HY) stage . Motor sCardiac metaiodobenzylguanidine (MIBG) scintigraphy was included in the Movement Disorder Society's supportive criteria for PD reflecting postganglionic cardiac sympathetic denervation . The subIn this study, patients with sleep disturbances and patients with EDS were defined as having a PDSS-2 total score ≥15  and an Epost-hoc multiple comparisons, the Mann–Whitney U-test with Bonferroni correction was conducted. Spearman rank correlation coefficients were used to assess univariate correlations between the PDSS-2 or ESS scores and clinical variables. We performed a subgroup analysis of patients on PD treatment to assess the correlation between LEDs and the PDSS-2 and ESS scores. The multivariable logistic regression model with patients with neither sleep disturbances nor EDS as a reference group was performed to control for the potentially confounding factors for patients with sleep disturbances and EDS. As independent variables, age, sex, disease duration, MDS-UPDRS part III, BDI-II, RBDSQ-J, and SCOPA-AUT scores were included according to previous reports (de novo patients only.We divided the patients into 4 groups: (1) patients with neither sleep disturbances nor EDS, (2) patients with sleep disturbances but no EDS, (3) patients with EDS but no sleep disturbances, and (4) patients with both sleep disturbances and EDS. To assess whether there was an overall difference, the Kruskal–Wallis test was used as appropriate to compare continuous variables. Similarly, the chi-square test was used to compare categorical variables. In  reports , 18–24. de novo PD patients, HY stage, MDS-UPDRS Part III score, MMSE score, olfactory test results, MIBG cardiac scintigraphy results and LED did not significantly differ between the groups. The disease duration seemed to be shorter in patients with neither sleep disturbances nor EDS than in patients in the other three groups, but the difference was not significant. Similarly, the MIBG cardiac scintigraphy results and olfactory scores tended to be lower in the patients with EDS, but the difference was not significant. The BDI-II, RBDSQ-J, and SCOPA-AUT scores and the percentage of patients with RLS showed significant differences among the groups. The BDI-II scores were higher in patients with sleep disturbances than in those without sleep disturbances, but they did not differ in those with or without EDS. Probable RBD was more frequently observed in patients with both EDS and sleep disturbances compared with those with neither EDS nor sleep disturbances, but there was no significant difference between patients with only either EDS or sleep disturbances and those with neither EDS nor sleep disturbances. The SCOPA-AUT score was lower in patients with neither EDS nor sleep disturbances than in patients in the other three groups. The percentage of dopamine agonist users tended to be higher in patients with only EDS than in patients with neither EDS nor sleep disturbances.The clinical characteristics of the patients with PD at baseline are presented in P = 0.302) in 9 patients, ropinirole  in 7 patients, pergolide (0.375 mg) in 1 patient, cabergoline  in 3 patients and the rotigotine patch  in 10 patients, with no significant differences in usage among the four groups. RLS was more prevalent in patients with either EDS or sleep disturbances than in those with neither EDS nor sleep disturbances. Regarding comorbid diseases, 10 patients had cardiovascular disease (P = 0.911), 20 had diabetes mellitus (P = 0.478), 39 had hypertension (P = 0.336), 26 had dyslipidemia (P = 0.288), 6 had thyroid disease (P = 0.646), and 3 had rheumatoid arthritis (P = 0.117), with no significant differences among the four groups according to the Kruskal–Wallis test.Dopamine agonists included pramipexole  and that the BDI-II  and RBDSQ-J scores  as well as the SCOPA-AUT score  were independent contributors in patients with both sleep disturbances and EDS (n = 57), the SCOPA-AUT score  was an independent contributor in patients with only sleep disturbances, and the BDI-II  and SCOPA-AUT scores  were independent contributors in patients with sleep disturbances and EDS.Multivariable logistic regression analysis of patients with neither sleep disturbances nor EDS as a reference group revealed that the SCOPA-AUT score was an independent contributor in patients with sleep disturbances [adjusted odds ratio (OR), 1.192; 95% confidence interval (CI), 1.065–1.333;  and EDS . In the In the present study, patients with sleep disturbances had higher depressive symptom scores than those without sleep disturbances, regardless of the presence or absence of EDS. More patients with probable RBD were observed in patients with both EDS and sleep disturbances than in patients with neither EDS nor sleep disturbances, but there was no significant difference between patients with only EDS or sleep disturbances. On the other hand, the SCOPA-AUT score was lower in the group with neither EDS nor sleep disturbances, and there was no significant difference between the group with both EDS and sleep disturbances and the group with only EDS or sleep disturbances. Univariate correlation analysis showed a positive relationship between the PDSS-2 or ESS score and the BDI-II, SCOPA-AUT and RBDSQ-J scores. Multivariable logistic regression analysis adjusted for age, sex, disease duration and the MDS-UPDRS part III score revealed that the SCOPA-AUT score was an independent contributor in patients with sleep disturbances or EDS and that the SCOPA-AUT, BDI-II, and RBDSQ-J scores were independent contributors in patients with sleep disturbances and EDS. In the subgroup analysis of untreated patients only, the SCOPA-AUT and BDI-II scores showed similar associations with patients with sleep disturbances and EDS. Taken together, autonomic symptoms were associated with patients with sleep disturbances or EDS, and depressive and RBD symptoms in addition to autonomic symptoms were associated with patients with both sleep disturbances and EDS.Previous studies have suggested that there is a reciprocal relationship between depression and sleep disturbances in patients with PD . AlthougWe previously reported that compared to those without probable RBD, patients with PD and probable RBD diagnosed with RBDSQ-J showed severe autonomic and sleep-related symptoms, including visual hallucinations and sleepiness . PreviouThe present study suggested that autonomic symptoms were associated with both nocturnal sleep disturbances and daytime sleepiness in patients with PD. Recently, Zhou et al.  reportedIn a previous study, the use of dopamine agonists was correlated with EDS , althougde novo patients. Among the 57 de novo patients, 19 (33%) had sleep disturbances, and 15 (26%) had EDS. Our results confirmed that a significant number of patients with PD experience sleep disturbances and EDS at an early stage, even though medication for PD has not yet been initiated. A subgroup analysis in this population confirmed that autonomic and depressive symptoms were independent contributors to sleep disturbances and EDS.The strength of the present study was the high percentage of The present study has some limitations. First, we lacked a control group. However, we conducted a comprehensive detailed clinical assessment, including cardiac MIBG scintigraphy and olfactory testing. Second, we assessed nocturnal sleep, daytime sleepiness and the definition of prodromal RBD using questionnaires. Although polysomnography and multiple sleep latency tests are objective and have strong diagnostic power, it is not practical to perform these procedures in all patients with PD. Third, lack of temporal information regarding the onset of RBD, conducted cardiac MIBG scintigraphy, and onset of motor symptoms made it impossible to discuss the difference between body-first and brain-first types. Fourth, the cross-sectional design of this study did not allow for interpretation of the direction of causality. Follow-up studies are needed.In our study, we found that autonomic symptoms were independent contributors in PD patients with sleep disturbances or EDS and that RBD symptoms and depressive symptoms in addition to autonomic symptoms were independent contributors in PD patients with both sleep disturbances and EDS.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.The studies involving human participants were reviewed and approved by Institutional Review Board of Dokkyo Medical University. The patients/participants provided their written informed consent to participate in this study.Conceptualization: HF, KO, TS, HS, NN, and KS. Data curation and writing—original draft: HF and KO. Formal analysis and supervision: KS. Investigation and methodology: TS and HS. Project administration: HF and NN. Writing—review and editing: TS, HS, and KS. All authors contributed to the article and approved the submitted version.


Integrating vocational rehabilitation and mental healthcare has shown effects on vocational outcomes during sick leave with common mental disorders. In a previous paper, we showed that a Danish integrated healthcare and vocational rehabilitation intervention (INT) had a surprisingly negative impact on vocational outcomes compared to service as usual (SAU) at 6- and 12-month follow-up. That was also the case with a mental healthcare intervention (MHC) tested in the same study. This article reports the 24-month follow-up results of that same study.A randomized, parallel-group, three-arm, multi-centre superiority trial was conducted to test the effectiveness of INT and MHC compared to SAU.In total, 631 persons were randomized. Contrary to our hypothesis, SAU showed faster return to work than both INT  and MHC  at 24-month follow-up. Overall, no differences were observed regarding mental health and functional level. Compared to SAU, we observed some health benefits of MHC, but not INT, at 6-month follow-up but not thereafter, and lower rates of employment at all follow-ups. Since implementation problems might explain the results of INT, we cannot conclude that INT is no better that SAU. The MHC intervention was implemented with good fidelity and did not improve return to work.This trial does not support the hypothesis that INT lead to faster return to work. However, implementation failure may explain the negative results. Long-term sick leave is a risk factor for exclusion from the labor market  and retuwww.sjweh.fi/article/4084, supplement 1). Eligible participants were adult sickness absentees (≥4 weeks), with a stress-related disorder. One outcome, recurrence of sick leave, was added at 24 months only. In INT, participants received best practice mental healthcare (BP-MH) and IBBIS vocational rehabilitation (IBBIS-VR). BP-MH was a stepped-care intervention, with treatment intensity determined by baseline symptom level. IBBIS-VR was based on the principles of the sharp-at work-intervention . Subgroup analyses were performed according to selected baseline values and time. Sensitivity analyses were conducted by imputing all missing data in best/worst case scenarios, and regarding return to stable work outcomes with different thresholds for what minimal duration constituted “stable” RTW .Analyses included 636 participants in total. table 2SAU yielded consistently better vocational outcomes across multiple measures at all follow-ups compared to both INT and MHC. Regarding symptoms, the slightly lower level of anxiety  in the MHC group compared to SAU at 24-month follow-up might be a chance finding since no other differences were seen across many symptom scales. Both MHC and INT failed to consistently improve the RTW process as hypothesized. Possible explanations for this failure include implementation, programme, or methodological failure .Indications of implementation failure : Through fidelity reviews, we observed a range of implementation issues: IBBIS-VR and the activities integrating the two intervention components showed lower implementation degree. As these two parts of the intervention were only part of INT, we can conclude that only interventions in the MHC group were implemented with high fidelity. True integration of the intervention components in INT was challenged by lack of trust and diverging norms and goals between the different sectors, as described elsewhere : Theory failure is indicated because MHC interventions were sufficiently implemented. Since BP-MH was delivered in INT as well, part of the failure of INT may also be due to theory failure.Indications of methodological failure : While time-to-RTW is the most common outcome measure in this research field, there is no consensus regarding specific definitions and measures of RTW  and evaluated and approved by the Regional Ethics Committees of the Capital Region (# H-16015724) and the Danish Data Protection Agency (#RHP-2016-006). It was conducted in accordance with Danish and European regulations. An IBBIS team member informed every participant about the objective of the study and the implications of participation, and all participants gave oral and written consent before enrolment.The trial was registered at


Cells can communicate with each other through extracellular vesicles (EVs), which are membrane-bound structures that transport proteins, lipids and nucleic acids. These structures have been found to mediate cellular differentiation and proliferation apoptosis, as well as inflammatory responses and senescence, among others. The cargo of these vesicles may include immunomodulatory molecules, which can then contribute to the pathogenesis of various diseases. By contrast, EVs secreted by mesenchymal stem cells (MSCs) have shown important immunosuppressive and regenerative properties. Moreover, EVs can be modified and used as drug carriers to precisely deliver therapeutic agents. In this review, we aim to summarize the current evidence on the roles of EVs in the progression and treatment of rheumatoid arthritis (RA) and osteoarthritis (OA), which are important and prevalent joint diseases with a significant global burden. Extracellular vesicles (EVs) are membrane-bound structures secreted by donor cells which can carry proteins, lipids and nucleic acids. Depending on their biogenesis and size, several types of EVs have been characterized, including exosomes, microvesicles, apoptotic bodies (ABs) and oncosomes. Exosomes originate from intraluminal vesicles after the fusion of multivesicular endosomes (MVEs) with the plasma membrane, while microvesicles are formed from the plasma membrane and contain cytoplasmic content. ABs are released by dying cells, and oncosomes are the products of neoplastic cells . These sRheumatoid arthritis is one of the most frequent inflammatory diseases, and causes structural and functional joint impairment. Nevertheless, the disease is also characterized by extraarticular manifestations, such as vasculitis, pulmonary involvement or rheumatoid nodules. Synovial inflammation, bone erosion and cartilage damage are key elements of RA pathogenesis. Multiple cells are involved in the progression of RA, including fibroblast-like synoviocytes (FLSs), macrophages, osteoclasts, neutrophils and T cells, among others . These cTo begin with, RA patients have elevated levels of EVs compared to healthy controls . FurtherThe pathogenesis of RA is associated with the formation of autoantibodies, such as RF, anti-citrullinated protein antibodies (ACPA), and anti-carbamylated protein antibodies. RF targets the Fc region of immunoglobulin G (IgG), while ACPA recognizes proteins that undergo post-translational citrullination. Collagen, vimentin and fibrinogen are some of the antigens undergoing this modification . StudiesEVs have been suggested to take part in the pathogenesis of autoimmune diseases. They are opsonized by CRP, which could drive the production of anti-CRP autoantibodies present in systemic lupus erythematosus (SLE) . MoreoveChronic inflammation is a major hallmark of RA. The pathological environment of the affected joint involves multiple cells, which interact with each other to stimulate the secretion of pro-inflammatory cytokines, chemokines and proteases. EVs can propagate the inflammation, as these structures may contain or express inflammatory molecules. For instance, EVs can carry TNF-α on their membrane and stimulate the nuclear factor kappa B (NF-κB) pathway, which is involved in the progression of inflammatory diseases . In RA, Additionally, despite the ability to directly promote inflammation by transporting TNF-α, synovial cells react to inflammatory stimuli and thus, secrete vesicles with particular cargo that can further regulate inflammation. For instance, the treatment of RA-FLS with TNF-α promotes the secretion of exosomes with miR-155-5p, miR-1307-3p, miR-323a-5p and miR-146a-5p . MiR-155Moreover, EVs secreted by synovial fibroblasts regulate T cell differentiation. For instance, exosomes containing miR-424 promote the differentiation of Th17 cells . Th17 ceThe chronic inflammation of RA is associated with an imbalance between bone formation and loss. This impairment is caused by the stimulation of osteoclasts through the binding of the receptor activator of NF-κB ligand (RANKL) to its receptor RANK, which is expressed on the precursor of osteoclasts . EVs seeIn RA, EVs seem to take part in the bone degradation by the modification of osteoclast/osteoblast balance. Exosomes isolated from RA-FLS co-cultured with osteoblasts suppress their differentiation . An in vPhysiologically, angiogenesis is an important mechanism taking part in tissue repair or wound healing. Nevertheless, in chronic inflammatory conditions present in RA, angiogenesis can facilitate the infiltration of immune cells to affected articular tissues. One of the key regulators of angiogenesis is the vascular endothelial growth factor (VEGF), a molecule which has been implicated in the progression of RA . RheumatIn the previous section, we discussed the pro-inflammatory roles of EVs and their contribution to the pathogenesis od RA. Nevertheless, studies showed that EVs can also suppress the disease progression. Interestingly, these structures are being used to facilitate the immunomodulatory properties of mesenchymal stem cells (MSCs). They are progenitor cells found in a variety of tissues such as adipose tissue (AD-MSCs), placenta, bone marrow (BM-MSC), human umbilical cord (UC-MSC) and gingiva (GMSCs) . MSCs afTo begin with, studies have shown that the application of MSC reduces the disease biomarkers, as well as inflammation in the animal arthritis models ,63. The FLSs play a key role in the progression of RA. As previously mentioned, they have an invasive character (“tumor-like appearance”) and secrete proteases and pro-inflammatory cytokines. Furthermore, these cells demonstrate enhanced survival and proliferation . As a reLimited studies have evaluated encapsulated lncRNAs or circRNAs and the use of their properties as sponges of miRNAs. For instance, the expression of miR-143-3p is upregulated in RA patients. The inhibition of miR-143-3p promoted apoptosis and inhibited the expression of pro-inflammatory mediators in human RA MH7A synovial cells. Therefore, it was suggested that miR-143-3p could become a therapeutic target . SubsequIn RA, multiple T cell subtypes with the potential to contribute to RA progression have been identified . MoreoveCIA models [Macrophages take part in RA progression, as they are the major source of the pro-inflammatory TNF-α. These cells can present different phenotypes, which is associated with their role in inflammation. M1 polarization is associated with pro-inflammatory responses that take part in the pathogenesis of RA . Thus, tA models . IL-4 isA models . InteresA models . TherefoProgrammed cell death protein 1 (PD-1) is a negative regulator of T cell responses. It binds to two ligands (PD-L1 and PD-L2), and its activation is associated with tolerance. In RA, it has been suggested that the PD-1/PD-L1 axis may be dysfunctional, and the ligand may not be available in the inflamed synovium . InteresOsteoarthritis is a whole joint disease with a significant prevalence and burden. The pathogenesis of OA involves changes in the synovium, articular cartilage, subchondral bone, muscles and ligaments. Metabolic, mechanical and inflammatory factors contribute to structural joint impairment . StudiesTo begin with, the cargo of exosomes derived from synovial fluid depends on the severity of OA. Exosomes isolated from the synovial fluid of patients with severe disease contain more cytokines and chemokines than those of patients with mild OA . OA-EVs COL2A1 and ACAN, key ECM markers, and promote MMP-13. Furthermore, exosomes from IL-1β-treated cells promote angiogenesis in human umbilical vein endothelial cells [Cartilage damage and low-grade inflammatory responses are important mechanisms taking place in the pathogenesis of OA ,116. Theal cells . In addial cells . Similaral cells . Similaral cells ,121. Ciral cells . In addial cells . Furtheral cells .Liu and colleagues found elevated concentrations of circ-PRKCH in serum exosomes of patients with OA. Silencing circRNA reduced inflammatory IL-6 and TNFα. CircPRKCH positively regulated ADAMTS5 expression through the sponging of miR-502-5p. Exosomes extracted from IL-1β-stimulated chondrocytes promoted the expression of circPRKCH in other chondrocytes, which confirms the ability of articular cells to transfer this RNA molecule using EVs . SimilarOA pathogenesis is associated with mineralization disturbances and cartilage calcification . StudiesPromoting the viability of chondrocytes represents one of possible strategies investigated in the treatment of OA. The pathogenesis of the disease is associated with chondrocyte death. Various cellular death processes have been identified. For instance, ferroptosis is an iron-dependent mechanism that has been identified in the pathogenesis of OA . ChondroSenescence is associated with morphological alterations, the accumulation of stress molecules and vacuolization. These alterations lead to the inhibition of growth and resistance to apoptosis, as well as to the production of senescence-associated secretory phenotypes (SASPs) which are inflammatory mediators and growth factors . CulturiConnexins (Cx) form hemichannels and gap junctions to mediate cellular communication. Importantly, they can take part in the process of senescence induction. Firstly, the Cx43 expression is upregulated in OA, and its downregulation decreases the expression of pro-inflammatory mediators and MMPs . IntriguAs previously mentioned, MSCs possess immunomodulatory and regenerative features which have been explored in OA. The use of these cells has been examined clinically. A phase IIb clinical trial investigated the intra-articular treatment with the autologous AD-MSCs in OA patients. The use of MSCs was associated with significantly improved Western Ontario and McMaster Universities Osteoarthritis index score after 6 months of treatment . RecentlDysregulated chondrocytes represent the major cells affected by and involved in OA. Therefore, suppressing the catabolic and senescent phenotype of chondrocytes could be of great interest. EVs derived from MSCs may represent an interesting strategy to achieve this goal. Stem-cell-derived EVs can promote chondrocyte proliferation and migration. Thus, these structures can suppress the negative effects induced by inflammatory conditions . The carInterestingly, the pretreatment of synovial MSC-derived exosomes with LPS further improved the beneficial effects of EVs. LPS stimulation induced alterations in the expression patterns of miRNAs. In a study by Duan et al., 64 and 18 miRNA molecules were downregulated and upregulated, respectively. Among RNA molecules, let-7b was found to target ADAMTS5 and suppress cartilage degradation . SimilarMSC-derived vesicles have been modified to include the bone morphogenic protein (BMP), which plays a role in cartilage repair and is considered chondroprotective. Importantly, recombinant BMPs are unstable, and studies have searched for an efficient delivery system . The traThe above-mentioned evidence highlights the beneficial role of MSC-derived EVs. Nevertheless, EVs secreted by different cells have also been found to have immunomodulatory properties. For instance, platelet-derived exosomes demonstrated significant positive effects on chondrocytes, as well as in OA animal models . Mir-126Importantly, Wang and colleagues point to the importance of a healthy synovium in OA. A co-culture of EVs derived from healthy FLSs with chondrocytes resulted in the enhanced expression of ECM markers, together with reduced pro-inflammatory cytokines. These effects could be mediated by miR-150-3p which inhibited the Trim14/NF-κB/IFNβ pathway . These oImpaired mitochondria have been suggested to play a role in OA progression. Various pathologies of these organelles have been observed, which could contribute to the disease progression. These disruptions include impairment of the mitochondrial respiratory chain, excessive generation of reactive oxygen species (ROS) and mitochondrial DNA damage, among others . ImportaPain is the hallmark symptom of OA. Inflammation and structural impairment of the elements of the joint contribute to pain development. Multiple ion channels and neuropeptides have been found to take part in the transduction of nociception. Furthermore, neuropathic mechanisms are also considered to play a role in OA pain processes . StudiesNerve growth factor (NGF) is a significant mediator of pain transduction that can be secreted by various cells involved in OA pathogenesis. Surprisingly, an in vivo study showed that silencing NGF provided pain relief but seemed to promote disease progression . InteresMoreover, studies have demonstrated that the calcitonin gene-related peptide (CGRP) takes part in pain transduction in OA. Its expression is elevated in OA patients and has been correlated with somatic pain intensity . He et aRecently, a trend towards precision therapy in medicine can be observed. Importantly, EVs can be modified to precisely deliver cargo that can suppress the disease. For instance, Liu and colleagues identified miR-223 as a potential agent in UC-MSCs-EVs which suppresses the NLRP3 inflammasome. Subsequently, EVs were engineered to include the collagen II binding peptide and overexpress miR-223. These structures amplified the beneficial effects of UC-MSCs-EVs. Thus, these structures contained a precise therapeutic agent and were equipped with proteins which allowed for precise targeting . MoreoveApoptosis is a programmed cell death mechanism during which cells release large ABs. Interestingly, these structures can be functional and modulate the number of cellular processes . ImportaOverall, the aim of this review was to describe the broad roles of EVs in the pathogenesis and treatment of RA and OA. Cells implicated in the progression of these diseases can communicate through EVs and induce various responses. In RA, these vesicles can regulate autoimmunity and inflammed bone impairment, as well as T cell differentiation. Nevertheless, these vesicles can also carry immunomodulatory cargo, which suppresses inflammation and the proliferation of RA-FLSs. Similarly, EVs have been found to act in both ways to promote or suppress OA. Cells can modulate each other’s activities by transporting ncRNA molecules, which mediate gene expression. Interestingly, these RNA molecules form a broad machinery of molecular interactions, which can impact various signaling pathways. EVs can be modified to overexpress a required molecule or contain a binding protein to improve bioavailability. The use of EVs can be an interesting continuation of ncRNA studies, as molecules downregulated in the RA/OA tissues could be loaded into exosomes and delivered to the affected joint. Furthermore, monitoring the content of EVs might help in predicting the treatment response . Current


This Research Topic collects the latest works focusing on approaches and analysis for medical neuroimaging data with limitations. With the increasing development of deep learning and artificial intelligence, it also attracts attention from researchers in medical and clinical fields , a Soft-DTW-based single-subject short-distance event-related potential averaging method is introduced for the low signal-to-noise ratio and feature extraction issues of N400 data. Specifically, N400 data is a time-domain EEG feature, which is thought to reflect the information processing ways of human brains. Leveraging the benefits of the differentiable and efficient Soft-DTW loss function, this work also involves performing partial Soft-DTW averaging within a single-subject range, effectively exploiting the advantages of the DTW distance. Furthermore, a Transformer-based recognition model incorporating location coding and a self-attention mechanism for contextual information is also presented. Finally, Softmax is selected as the classifier. Numerous experiments on the N400 public dataset demonstrate the performance of the proposed model and averaging method. These results validate the capabilities of the proposed approach in effectively handling ERP data and improving classification accuracy.In the first article of this Research Topic , some machine learning methods are adopted for forecasting stroke recurrence within 1 year in patients who have experienced acute ischemic stroke (AIS). Recurrent strokes contribute to approximately 25–30% of all preventable strokes, making the development of an accurate predictive tool crucial for identifying high-risk patients and implementing timely preventive measures. Univariate and multivariate logistic regression (LR) analyses are employed to identify potential risk factors associated with stroke recurrence. The dataset is randomly split into a training and a test set with a 7:3 ratio. Subsequently, six machine learning models are established, including random forest (RF), Naive Bayes model (NBC), decision tree (DT), extreme gradient boosting (XGB), gradient boosting machine (GBM), and LR. To determine the model with the most robust prediction performance, 10-fold cross-validation, receiver operating characteristic (ROC) curves, and SHapley Additive exPlanations (SHAP) are utilized. Finally, a user-friendly web calculator is built for better visualization and interpretation.In the second article of this Research Topic , federated learning (FL) is adopted for multiple sclerosis (MS) lesion segmentation. Specifically, FL aims to be widely applied in the medical image analysis field, which could learn from multi-site clients and keep the privacy of local sites. FL has shown promising applications in various domains, but its potential in neuroimage analysis tasks, like lesion segmentation in MS, is yet to be fully optimized. The challenges arise from the variability in lesion characteristics caused by using different scanners and acquisition parameters across multiple data sources. In this work, learnable weights are allocated according to the performance of each site. Furthermore, a weighted segmentation loss function is also introduced based on the lesion volume of each site. Two datasets, including one public and one clinical dataset, are adopted to verify the segmentation performance of the proposed framework.In the third article of this Research Topic , the authors focus on medical prediction from missing data. Unlike natural image datasets, missing data is a general issue in the medical image field. In the current research, the uncertainty associated with imputation can lead the model to overfit the observed data distribution, subsequently impacting its generalization performance negatively. While R-Drop is a potent regularization method for training deep neural networks, it cannot distinguish between positive and negative samples, thereby hindering the model's capacity to learn robust representations. In this work, a modified R-Drop method with negative regularization is proposed to boost the generalization and performance of medical image processing. The modified framework introduces a deliberate inconsistency between the output distributions of positive and negative samples. Notably, it also introduces a max-minus negative sampling technique that enhances model diversity by subtracting the mini-batch values from the maximum in-batch values to create negative samples. Three real-world medical prediction datasets, including both missing and complete data, are involved in the experimental parts.In the fourth article of this Research Topic (Concerning the above papers in this Research Topic, they provide some novel ideas and approaches for medical neuroimaging data with limitations. We hope that the readers can be inspired by these state-of-the-art works and introduce more effective and powerful ways in neuroscience.ZT: Writing—original draft. ML: Writing—review and editing. RH: Writing—review and editing. KD: Writing—review and editing.