Analysis of PD-L1 promoter methylation combined with immunogenic context in pancreatic ductal adenocarcinoma.

Despite the successful application of programmed cell death ligand 1 (PD-L1)-blocking strategies in some types of cancers and well-established prognostic indicators in pancreatic ductal adenocarcinoma (PDAC), the biological and clinical implications of the methylation status of PD-L1/PD-L2 in PDAC remain largely unknown. Therefore, this study aimed to explore the biological role of PD-L1/PD-L2 methylation and its association with clinicopathological features, clinical outcomes, and the immune microenvironment by analyzing the data on PD-L1/PD-L2 methylation and mRNA expression in PDAC cohorts obtained from the Cancer Genome Atlas and International Cancer Genome Consortium. The correlation between PD-L1 promoter methylation and PD-L1 expression and survival was further validated in an independent validation cohort (Peking Union Medical College Hospital [PUMCH] cohort) using pyrosequencing and immunohistochemistry. These results demonstrated that hypomethylation of the PD-L1 promoter was strongly associated with upregulated PD-L1 expression and shorter overall survival in PDAC. Multivariate Cox regression analyses revealed that the PD-L1 promoter methylation was an independent prognostic factor. PD-L1 promoter hypomethylation and high expression were related to aggressive clinical phenotypes. Moreover, both PD-L1 and PD-L2 methylation correlated with immune cell infiltration and the expression of immune checkpoint genes. PD-L1 promoter methylation status was further validated as an independent prognostic biomarker in patients with PDAC using the PUMCH cohort. The prognostic significance of PD-L1 promoter methylation was more discriminative in tumors with perineural/lymphovascular invasion and distant metastasis than in those without perineural/lymphovascular invasion and distant metastasis. In summary, the methylation status of the PD-L1 promoter is a promising biomarker for survival outcomes, immune infiltration, and the potential immune benefits of immunotherapy in PDAC.

Disease Progression and Multiparametric Imaging Characteristics of Spinocerebellar Ataxia Type 3 With Spastic Paraplegia.

Background and Objectives: Spinocerebellar ataxia type 3 (SCA3) is a hereditary ataxia that occurs worldwide. Clinical patterns were observed, including the one characterized by marked spastic paraplegia. This study investigated the clinical features, disease progression, and multiparametric imaging aspects of patients with SCA3. Methods: We retrospectively analyzed 249 patients with SCA3 recruited from the Organization for Southeast China for cerebellar ataxia research between October 2014 and December 2020. Of the 249 patients, 145 were selected and assigned to 2 groups based on neurologic examination: SCA3 patients with spastic paraplegia (SCA3-SP) and SCA3 patients with nonspastic paraplegia (SCA3-NSP). Participants underwent 3.0-T brain MRI examinations, and voxel-wise and volume-of-interest-based approaches were used for the resulting images. A tract-based spatial statistical approach was used to investigate the white matter (WM) alterations using diffusion tensor imaging, neurite orientation dispersion, and density imaging metrics. Multiple linear regression analyses were performed to compare the clinical and imaging parameters between the 2 groups. The longitudinal data were evaluated using a linear mixed-effects model. Results: Forty-three patients with SCA3-SP (mean age, 37.58years ± 11.72 [SD]; 18 women) and 102 patients with SCA3-NSP (mean age, 47.42years ± 12.50 [SD]; 39 women) were analyzed. Patients with SCA3-SP were younger and had a lower onset age but a larger cytosine-adenine-guanine repeat number, as well as higher clinical severity scores (all corrected p < 0.05). The estimated progression rates of the Scale for the Assessment and Rating of Ataxia (SARA) and International Cooperative Ataxia Rating Scale scores were higher in the SCA3-SP subgroup than in the SCA3-NSP subgroup (SARA, 2.136 vs 1.218 points; ICARS, 5.576 vs 3.480 points; both p < 0.001). In addition, patients with SCA3-SP showed gray matter volume loss in the precentral gyrus with a decreased neurite density index in the WM of the corticospinal tract and cerebellar peduncles compared with patients with SCA3-NSP. Discussion: SCA3-SP differs from SCA3-NSP in clinical features, multiparametric brain imaging findings, and longitudinal follow-up progression.

Differential Regulation of DC Function, Adaptive Immunity, and MyD88 Dependence by MF59 and AS03-like Adjuvants.

MF59 and AS03 are squalene emulsion-based vaccine adjuvants with similar compositions and droplet sizes. Despite their broad use in licensed influenza vaccines, few studies compared their adjuvant effects and action mechanisms side by side. Considering the majority of adjuvants act on dendritic cells (DCs) to achieve their adjuvant effects, this study compared MF59 and AS03-like adjuvants (AddaVax and AddaS03, respectively) to enhance antigen uptake, DC maturation, ovalbumin (OVA) and seasonal influenza vaccine-induced immune responses. Considering MF59 was reported to activate MyD88 to mediate its adjuvant effects, this study also investigated whether the above-explored adjuvant effects of AddaVax and AddaS03 depended on MyD88. We found AddaVax more potently enhanced antigen uptake at the local injection site, while AddaS03 more potently enhanced antigen uptake in the draining lymph nodes. AddaS03 but not AddaVax stimulated DC maturation. Adjuvant-enhanced antigen uptake was MyD88 independent, while AddaS03-induced DC maturation was MyD88 dependent. AddaVax and AddaS03 similarly enhanced OVA-induced IgG and subtype IgG1 antibody responses as well as influenza vaccine-induced hemagglutination inhibition antibody titers, whileAddaS03 more potently enhanced OVA-specific IgG2c antibody responses. Both adjuvants depended on MyD88 to enhance vaccine-induced antibody responses, while AddaVax depended more on MyD88 to achieve its adjuvant effects. Our study reveals similarities and differences of the two squalene emulsion-based vaccine adjuvants, contributing to our improved understanding of their action mechanisms.

Functionalizing Sgc8-Paclitaxel Conjugates with F-Base Modifications: Targeted Drug Delivery with Optimized Cardiac Safety.

Recent advancements in cancer treatment have improved patient prognoses, but chemotherapy-induced cardiotoxicity remains a prevalent concern. This study explores the potential of F-base-modified aptamers for targeted drug delivery, focusing on their impact on cardiotoxicity. From the phosphoramidite, F-base functionalized Sgc8-F23 was prepared in an automated and programmable way, which was further reacted with Paclitaxel (PTX) to give the F-base modified aptamer Sgc8-paclitaxel conjugates (Sgc8-F23-PTX) efficiently. The conjugate exhibits prolonged circulation time and enhanced efficacy as precision anticancer drug delivery system. Echocardiographic assessments reveal no exacerbation of cardiac dysfunction post-Acute Myocardial Infarction (AMI), and no pathological changes or increased apoptosis in non-infarcted cardiac regions. Autophagy pathway analysis shows no discernible differences in Sgc8-F23-PTX-treated cardiomyocytes compared to controls, contrasting with increased autophagy with Nanoparticle albumin-bound -Paclitaxel (Nab-PTX). Similarly, apoptosis analysis shows no significant distinctions. Moreover, Sgc8-F23-PTX exhibits no inhibitory effects on hERG, hNav1.5, or hCav1.2 channels. These findings suggest the safety and efficacy of F-base-modified Sgc8 aptamers for targeted drug delivery, holding potential clinical applications. Further research is warranted for clinical translation and exploration of other drug carriers.

Sample size and power calculation for testing treatment effect heterogeneity in cluster randomized crossover designs.

The cluster randomized crossover design has been proposed to improve efficiency over the traditional parallel-arm cluster randomized design. While statistical methods have been developed for designing cluster randomized crossover trials, they have exclusively focused on testing the overall average treatment effect, with little attention to differential treatment effects across subpopulations. Recently, interest has grown in understanding whether treatment effects may vary across pre-specified patient subpopulations, such as those defined by demographic or clinical characteristics. In this article, we consider the two-treatment two-period cluster randomized crossover design under either a cross-sectional or closed-cohort sampling scheme, where it is of interest to detect the heterogeneity of treatment effect via an interaction test. Assuming a patterned correlation structure for both the covariate and the outcome, we derive new sample size formulas for testing the heterogeneity of treatment effect with continuous outcomes based on linear mixed models. Our formulas also address unequal cluster sizes and therefore allow us to analytically assess the impact of unequal cluster sizes on the power of the interaction test in cluster randomized crossover designs. We conduct simulations to confirm the accuracy of the proposed methods, and illustrate their application in two real cluster randomized crossover trials.

Effect of Regional Brain Activity Following Repeat Transcranial Magnetic Stimulation in SCA3: A Secondary Analysis of a Randomized Clinical Trial.

Repetitive transcranial magnetic stimulation (rTMS), a noninvasive neuroregulatory technique used to treat neurodegenerative diseases, holds promise for spinocerebellar ataxia type 3 (SCA3) treatment, although its efficacy and mechanisms remain unclear. This study aims to observe the short-term impact of cerebellar rTMS on motor function in SCA3 patients and utilize resting-state functional magnetic resonance imaging (RS-fMRI) to assess potential therapeutic mechanisms. Twenty-two SCA3 patients were randomly assigned to receive actual rTMS (AC group, n = 11, three men and eight women; age 32-55 years) or sham rTMS (SH group, n = 11, three men and eight women; age 26-58 years). Both groups underwent cerebellar rTMS or sham rTMS daily for 15 days. The primary outcome measured was the ICARS scores and parameters for regional brain activity. Compared to baseline, ICARS scores decreased more significantly in the AC group than in the SH group after the 15-day intervention. Imaging indicators revealed increased Amplitude of Low Frequency Fluctuation (ALFF) values in the posterior cerebellar lobe and cerebellar tonsil following AC stimulation. This study suggests that rTMS enhances motor functions in SCA3 patients by modulating the excitability of specific brain regions and associated pathways, reinforcing the potential clinical utility of rTMS in SCA3 treatment. The Chinese Clinical Trial Registry identifier is ChiCTR1800020133.

The Role of Different Systemic Inflammatory Indexes Derived from Complete Blood Count in Differentiating Acute from Chronic Calculus Cholecystitis and Predicting Its Severity.

Purpose: This study aimed to evaluate the diagnostic value of the different Complete blood count-derived systemic inflammation indexes, including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and neutrophil to lymphocyte × platelet ratio (NLPR) in differential diagnosing the chronic calculus cholecystitis (CCC) and the acute calculus cholecystitis (ACC), as well as determining the severity of ACC. Patients and Methods: 105 CCC and 88 ACC patients were enrolled. NLR, PLR, SII, and NLPR were evaluated in both cohorts as well as in different severity levels of ACC. The severity of ACC was determined based on the Tokyo Guidelines. Receiver operating characteristic (ROC) curve and Univariate/multivariate regression analyses were conducted. Results: The levels of NLR, PLR, SII, and NLPR were significantly higher in the ACC group compared to the CCC group. The optimal cutoff values for NLR, PLR, SII, and NLPR were determined to be 3.89, 144.7, 896.8, and 0.031 respectively. NLR>3.89 demonstrates the highest predictive capability with an AUC of 0.801 and a sensitivity of 72.73%. Multivariate analysis showed that NLR>3.89 (OR: 4.169, p = 0.004) and NLPR>0.031 (OR: 4.304, p = 0.005) were dominant in distinguishing ACC from CCC. In ACC patients, the levels of NLR, SII, and NLPR were significantly higher in the Moderate to Severe-degree ACC (MS-ACC) group than in Mild-Degree ACC (M-ACC). NLPR > 0.044 exhibited the highest predictive ability with an AUC of 0.778 and a specificity of 91.67%. Multivariate analysis showed that NLR>6.399 (OR: 10.308, p = 0.000) was a possible independent prognostic factor for accessing the severity of ACC. Conclusion: Systemic inflammation indexes can be useful in predicting the risk of ACC and MS-ACC. NLR demonstrates the best distinguishing power and sensitivity for distinguishing ACC from CCC, while NLPR shows the best predictive power and specificity for predicting the severity of ACC.

Early Predicting Osteogenic Differentiation of Mesenchymal Stem Cells Based on Deep Learning Within One Day.

Osteogenic differentiation of mesenchymal stem cells (MSCs) is proposed to be critical for bone tissue engineering and regenerative medicine. However, the current approach for evaluating osteogenic differentiation mainly involves immunohistochemical staining of specific markers which often can be detected at day 5-7 of osteogenic inducing. Deep learning (DL) is a significant technology for realizing artificial intelligence (AI). Computer vision, a branch of AI, has been proved to achieve high-precision image recognition using convolutional neural networks (CNNs). Our goal was to train CNNs to quantitatively measure the osteogenic differentiation of MSCs. To this end, bright-field images of MSCs during early osteogenic differentiation (day 0, 1, 3, 5, and 7) were captured using a simple optical phase contrast microscope to train CNNs. The results showed that the CNNs could be trained to recognize undifferentiated cells and differentiating cells with an accuracy of 0.961 on the independent test set. In addition, we found that CNNs successfully distinguished differentiated cells at a very early stage (only 1 day). Further analysis showed that overall morphological features of MSCs were the main basis for the CNN classification. In conclusion, MSCs differentiation detection can be achieved early and accurately through simple bright-field images and DL networks, which may also provide a potential and novel method for the field of cell detection in the near future.

Mechanical memory based on chromatin and metabolism remodeling promotes proliferation and smooth muscle differentiation in mesenchymal stem cells.

Stem cells respond and remember mechanical cues from the microenvironment, which modulates their therapeutic effects. Chromatin organization and energy metabolism regulate the stem cell fate induced by mechanical cues. However, the mechanism of mechanical memory is still unclear. This study aimed to investigate the effects of mechanical amplitude, frequency, duration, and stretch cycle on mechanical memory in mesenchymal stem cells. It showed that the amplitude was the dominant parameter to the persistence of cell alignment. F-actin, paxillin, and nuclear deformation are more prone to be remolded than cell alignment. Stretching induces transcriptional memory, resulting in greater transcription upon subsequent reloading. Cell metabolism displays mechanical memory with sustained mitochondrial fusion and increased ATP production. The mechanical memory of chromatin condensation is mediated by histone H3 lysine 27 trimethylation, leading to much higher smooth muscle differentiation efficiency. Interestingly, mechanical memory can be transmitted based on direct cell-cell interaction, and stretched cells can remodel the metabolic homeostasis of static cells. Our results provide insight into the underlying mechanism of mechanical memory and its potential benefits for stem cell therapy.

A BAYESIAN MACHINE LEARNING APPROACH FOR ESTIMATING HETEROGENEOUS SURVIVOR CAUSAL EFFECTS: APPLICATIONS TO A CRITICAL CARE TRIAL.

Assessing heterogeneity in the effects of treatments has become increasingly popular in the field of causal inference and carries important implications for clinical decision-making. While extensive literature exists for studying treatment effect heterogeneity when outcomes are fully observed, there has been limited development in tools for estimating heterogeneous causal effects when patient-centered outcomes are truncated by a terminal event, such as death. Due to mortality occurring during study follow-up, the outcomes of interest are unobservable, undefined, or not fully observed for many participants in which case principal stratification is an appealing framework to draw valid causal conclusions. Motivated by the Acute Respiratory Distress Syndrome Network (ARDSNetwork) ARDS respiratory management (ARMA) trial, we developed a flexible Bayesian machine learning approach to estimate the average causal effect and heterogeneous causal effects among the always-survivors stratum when clinical outcomes are subject to truncation. We adopted Bayesian additive regression trees (BART) to flexibly specify separate mean models for the potential outcomes and latent stratum membership. In the analysis of the ARMA trial, we found that the low tidal volume treatment had an overall benefit for participants sustaining acute lung injuries on the outcome of time to returning home but substantial heterogeneity in treatment effects among the always-survivors, driven most strongly by biologic sex and the alveolar-arterial oxygen gradient at baseline (a physiologic measure of lung function and degree of hypoxemia). These findings illustrate how the proposed methodology could guide the prognostic enrichment of future trials in the field.

Unlocking the Potential of Pterostilbene: A Pharmaceutical Element for Aptamer-Based Nanomedicine.

Natural compounds like pterostilbene (PTE) have gained recognition for their various biological activities and potential health benefits. However, challenges related to bioavailability and limited clinical efficacy have prompted efforts to strengthen their therapeutic potential. To meet these challenges, we herein rationally designed and successfully synthesized a pharmaceutical phosphoramidite that allows for the programmable incorporation of PTE into oligonucleotides. The resultant aptamer-PTE conjugate can selectively bind to cancer cells, leading to a specific internalization and drug release. Moreover, compared with free PTE, the conjugate exhibits superior cytotoxicity in cancer cells. Specifically, in a zebrafish xenograft model, the nanomedicine effectively inhibits tumor growth and neovascularization, highlighting its potential for targeted antitumor therapy. This approach presents a promising avenue for harnessing the therapeutic potential of natural compounds via a nanomedicine solution.

Synthesis and Selective Anticancer Activity Evaluation of 2-phenylacrylonitrile Derivatives as Tubulin Inhibitors.

OBJECTIVE: This study aimed at synthesizing 13 series of novel derivatives with 2-phenylacrylonitrile, evaluating antitumor activity both in vivo and in vitro, and obtaining novel tubulin inhibitors. METHOD: The 13 series of 2-phenylacrylonitrile derivatives were synthesized by Knoevenagel condensation and the anti-proliferative activities were determined by MTT assay. The cell cycle and apoptosis were analyzed by flow cytometer. Quantitative cell migration was performed using 24-well Boyden chambers. The proteins were detected by western blotting. in vitro kinetics of microtubule assembly was measured using ELISA kit for Human ß-tubulin (TUBB). Molecular docking was done by Discovery Studio (DS) 2017 Client online tool. RESULTS: Among the derivatives, compound 1g2a possessed strong inhibitory activity against HCT116 (IC50 = 5.9 nM) and BEL-7402 (IC50 = 7.8 nM) cells. Compound 1g2a exhibited better selective antiproliferative activities and specificities than all the positive control drugs, including taxol. Compound 1g2a inhibited proliferation of HCT116 and BEL-7402 cells by arresting them in the G2/M phase of the cell cycle, inhibited the migration of HCT116 and BEL-7402 cells and the formation of cell colonies. Compound 1g2a showed excellent tubulin polymerization inhibitory activity on HCT116 and BEL-7402 cells. The results of molecular docking analyses showed that 1g2a may inhibit tubulin to exert anticancer effects. CONCLUSION: Compound 1g2a shows outstanding antitumor activity both in vivo and in vitro and has the potential to be further developed into a highly effective antitumor agent with little toxicity to normal tissues.

Case report: Beneficial effects of visual cortex tDCS stimulation combined with visual training in patients with visual field defects.

Background: Visual field defect (VFD) refers to the phenomenon that the eye is unable to see a certain area within the normal range of vision, which may be caused by eye diseases, neurological diseases and other reasons. Transcranial direct current stimulation (tDCS) is expected to be an effective treatment for the recovery or partial recovery of VFD. This paper describes the potential for tDCS in combination with visual retraining strategies to have a positive impact on vision recovery, and the potential for neuroplasticity to play a key role in vision recovery. Methods: This case report includes two patients. Patient 1 was diagnosed with a right occipital hemorrhage and homonymous hemianopia. Patient 2 had multiple facial fractures, a contusion of the right eye, and damage to the optic nerve of the right eye, which was diagnosed as a peripheral nerve injury (optic nerve injury). We administered a series of treatments to two patients, including transcranial direct current stimulation; visual field restoration rehabilitation: paracentric gaze training, upper and lower visual field training, VR rehabilitation, and perceptual training. One time per day, 5 days per week, total 6 weeks. Results: After 6 weeks of visual rehabilitation and tDCS treatment, Patient 1 Humphrey visual field examination showed a significant improvement compared to the initial visit, with a reduction in the extent of visual field defects, increased visual acuity, and improvement in most visual functions. Patient 2 had an expanded visual field, improved visual sensitivity, and substantial improvement in visual function. Conclusion: Our case reports support the feasibility and effectiveness of tDCS combined with visual rehabilitation training in the treatment of occipital stroke and optic nerve injury settings.

An EEG motor imagery dataset for brain computer interface in acute stroke patients.

The brain-computer interface (BCI) is a technology that involves direct communication with parts of the brain and has evolved rapidly in recent years; it has begun to be used in clinical practice, such as for patient rehabilitation. Patient electroencephalography (EEG) datasets are critical for algorithm optimization and clinical applications of BCIs but are rare at present. We collected data from 50 acute stroke patients with wireless portable saline EEG devices during the performance of two tasks: 1) imagining right-handed movements and 2) imagining left-handed movements. The dataset consists of four types of data: 1) the motor imagery instructions, 2) raw recording data, 3) pre-processed data after removing artefacts and other manipulations, and 4) patient characteristics. This is the first open dataset to address left- and right-handed motor imagery in acute stroke patients. We believe that the dataset will be very helpful for analysing brain activation and designing decoding methods that are more applicable for acute stroke patients, which will greatly facilitate research in the field of motor imagery-BCI.

Semi-supervised breast cancer pathology image segmentation based on fine-grained classification guidance.

Breast cancer pathological image segmentation (BCPIS) holds significant value in assisting physicians with quantifying tumor regions and providing treatment guidance. However, achieving fine-grained semantic segmentation remains a major challenge for this technology. The complex and diverse morphologies of breast cancer tissue structures result in high costs for manual annotation, thereby limiting the sample size and annotation quality of the dataset. These practical issues have a significant impact on the segmentation performance. To overcome these challenges, this study proposes a semi-supervised learning model based on classification-guided segmentation. The model first utilizes a multi-scale convolutional network to extract rich semantic information and then employs a multi-expert cross-layer joint learning strategy, integrating a small number of labeled samples to iteratively provide the model with class-generated multi-cue pseudo-labels and real labels. Given the complexity of the breast cancer samples and the limited sample quantity, an innovative approach of augmenting additional unlabeled data was adopted to overcome this limitation. Experimental results demonstrate that, although the proposed model falls slightly behind supervised segmentation models, it still exhibits significant progress and innovation. The semi-supervised model in this study achieves outstanding performance, with an IoU (Intersection over Union) value of 71.53%. Compared to other semi-supervised methods, the model developed in this study demonstrates a performance advantage of approximately 3%. Furthermore, the research findings indicate a significant correlation between the classification and segmentation tasks in breast cancer pathological images, and the guidance of a multi-expert system can significantly enhance the fine-grained effects of semi-supervised semantic segmentation.

An Efficient Group Federated Learning Framework for Large-Scale EEG-Based Driver Drowsiness Detection.

To avoid traffic accidents, monitoring the driver's electroencephalogram (EEG) signals to assess drowsiness is an effective solution. However, aggregating the personal data of these drivers may lead to insufficient data usage and pose a risk of privacy breaches. To address these issues, a framework called Group Federated Learning (Group-FL) for large-scale driver drowsiness detection is proposed, which can efficiently utilize diverse client data while protecting privacy. First, by arranging the clients into different levels of groups and gradually aggregating their model parameters from low-level groups to high-level groups, communication and time costs are reduced. In addition, to solve the problem of notable variations in EEG signals among different clients, a global-personalized deep neural network is designed. The global model extracts shared features from various clients, while the personalized model extracts fine-grained features from each client and outputs classification results. Finally, to address special issues such as scale/category imbalance and data pollution, three checking modules are designed for adjusting grouping, evaluating client data, and effectively applying personalized models. Through extensive experimentation, the effectiveness of each component within the framework was validated, and a mean accuracy, F1-score, and Area Under Curve (AUC) of 81.0%, 82.0%, and 87.9% was achieved, respectively, on a publicly available dataset comprising 11 subjects.

Cognitive impairment associated with cerebellar volume loss in spinocerebellar ataxia type 3.

BACKGROUND: Many neuroscience and neurology studies have forced a reconsideration of the traditional motor-related scope of cerebellar function, which has now expanded to include various cognitive functions. Spinocerebellar ataxia type 3 (SCA3; the most common hereditary ataxia) is neuropathologically characterized by cerebellar atrophy and frequently presents with cognitive impairment. OBJECTIVE: To characterize cognitive impairment in SCA3 and investigate the cerebellum-cognition associations. METHODS: This prospective, cross-sectional cohort study recruited 126 SCA3 patients and 41 healthy control individuals (HCs). Participants underwent a brain 3D T1-weighted images as well as neuropsychological tests. Voxel-based morphometry (VBM) and region of interest (ROI) approaches were performed on the 3D T1-weighted images. CERES was used to automatically segment cerebellums. Patients were grouped into cognitively impaired (CI) and cognitively preserved (CP), and clinical and MRI parameters were compared. Multivariable regression models were fitted to examine associations between cerebellar microstructural alterations and cognitive domain impairments. RESULTS: Compared to HCs, SCA3 patients showed cognitive domain impairments in information processing speed, verbal memory, executive function, and visuospatial perception. Between CI and CP subgroups, the CI subgroup was older and had lower education, as well as higher severity scores. VBM and ROI analyses revealed volume loss in cerebellar bilateral lobule VI, right lobule Crus I, and right lobule IV of the CI subgroup, and all these cerebellar lobules were associated with the above cognitive domain impairments. CONCLUSIONS: Our findings demonstrate the multiple cognitive domain impairments in SCA3 patients and indicate the responsible cerebellar lobules for the impaired cognitive domain(s).

Planning stepped wedge cluster randomized trials to detect treatment effect heterogeneity.

Stepped wedge design is a popular research design that enables a rigorous evaluation of candidate interventions by using a staggered cluster randomization strategy. While analytical methods were developed for designing stepped wedge trials, the prior focus has been solely on testing for the average treatment effect. With a growing interest on formal evaluation of the heterogeneity of treatment effects across patient subpopulations, trial planning efforts need appropriate methods to accurately identify sample sizes or design configurations that can generate evidence for both the average treatment effect and variations in subgroup treatment effects. To fill in that important gap, this article derives novel variance formulas for confirmatory analyses of treatment effect heterogeneity, that are applicable to both cross-sectional and closed-cohort stepped wedge designs. We additionally point out that the same framework can be used for more efficient average treatment effect analyses via covariate adjustment, and allows the use of familiar power formulas for average treatment effect analyses to proceed. Our results further sheds light on optimal design allocations of clusters to maximize the weighted precision for assessing both the average and heterogeneous treatment effects. We apply the new methods to the Lumbar Imaging with Reporting of Epidemiology Trial, and carry out a simulation study to validate our new methods.

Th1/Th2 cytokine profile in patients with acute and chronic calculus cholecystitis.

Relatively little is known about the relationship between Th1/Th2 cytokines and calculus cholecystitis (CC). The purpose of this study was to investigate the correlation between serum Th1 and Th2 cytokine expression and CC, including both acute and chronic cases. In total, 102 patients with chronic calculous cholecystitis (CCC), 64 patients with acute calculous cholecystitis (ACC), and 55 healthy controls (HCs) were recruited for the study. Serum concentration of Th1 (IL-2, TNF-α, IFN-γ) and Th2 cytokines (IL-4, IL-6, IL-10) was measured at admission and on the fifth day after cholecystectomy using flow cytometry. In addition, the ratio of IL-6/IL-10 was calculated. Correlation of the corresponding factors was then analysed, and univariate and multivariate Cox regression analyses were performed to identify independent markers of ACC severity. Compared to HCs, CCC patients exhibited significantly elevated expression levels of IL-6 and IL-10, while ACC patients demonstrated higher expression of IL-2, TNF-α, and IL-6/ IL-10 in addition to IL-6, and IL-10. In ACC patients, there was a strong positive correlation between IL-6 and IL-10 concentration, the expression of IL-2 was observed to positively correlate with serum ALT and AST concentration, and TNF-α expression positively correlated with the duration of hospitalization. Moreover, patients with moderate-to-severe ACC presented with higher expression of IL-10 compared to those with mild ACC. Cox regression analysis confirmed that IL-10 and IL-6 were independent factors for the severity of ACC. Following surgery, the levels of IL-6 and IL-6/IL-10 significantly decreased but did not fully return to baseline levels in ACC patients. Our study reveals atypical Th1/Th2 cytokine expression profiles in patients with acute and chronic CC, and further highlights the significant potential of these cytokines, particularly IL-6 and IL-10, in assessing the severity and progression of CC.

Short-Term Efficacy of Cerebello-spinal tDCS and Body Weight-Supported Treadmill Training in the Hypertrophic Olivary Degeneration: a Rare Case Report.

The present case study reported a patient diagnosed with hypertrophic olivary degeneration, a rare condition characterized by a trans-neuronal degeneration and signal enhancement in T2-weighted images on magnetic resonance imaging, usually caused by cerebral hemorrhage, cerebral infarction, and trauma. Furthermore, the relevant literature review was performed. The existing pharmacological treatment has limited clinical benefits on the patient. Since spontaneous remission hardly occurs in the disease, there are no other effective treatments. In this case, the patient was a 55-year-old Chinese male who presented progressive gait difficulty for several months due to both-sided ataxia. Neurological examination revealed upper extremity and lower limb bilateral spasticity, ataxia, slurred speech, and dysmetria. Therefore, our study treated the patient through the inventive application of cerebello-spinal transcranial direct current stimulation and body weight-supported treadmill training. After a 4-week treatment, the patient could walk independently, without aid, speeding up by 7%, as well as the ataxia symptoms, and balance has improved significantly. It was demonstrated in this case report that the combination of cerebello-spinal tDCS and body weight-supported treadmill training can be an effective treatment for patients with Hypertrophic olivary degeneration.