Search Results (901)

The integration of machine learning (ML) into industrial automation is fundamentally reshaping how manufacturing systems are monitored, inspected, and optimized. By applying machine learning to real-time sensor data and operational histories, advanced models enable proactive fault prediction, intelligent inspection, and dynamic process control—directly enhancing system reliability, product quality, and efficiency. This review explores the transformative role of ML across three key domains: Predictive Maintenance (PdM), Quality Control (QC), and Process Optimization (PO). It also analyzes how Digital Twin (DT) and Edge AI technologies are expanding the practical impact of ML in these areas. Our analysis reveals a marked rise in deep learning, especially convolutional and recurrent architectures, with a growing shift toward real-time, edge-based deployment. The paper also catalogs the datasets used, the tools and sensors employed for data collection, and the industrial software platforms supporting ML deployment in practice. This review not only maps the current research terrain but also highlights emerging opportunities in self-learning systems, federated architectures, explainable AI, and themes such as self-adaptive control, collaborative intelligence, and autonomous defect diagnosis—indicating that ML is poised to become deeply embedded across the full spectrum of industrial operations in the coming years. Full article

Background/Objectives: Pneumatic dilation (PD) is a widely used treatment modality in the management of achalasia. It is particularly relevant in regions where many centers lack access to advanced therapeutic modalities. Therefore, we aimed to assess the effectiveness and safety of PD in our local region. Methods: This study retrospectively analyzed patients with achalasia that underwent PD from 1/2013 to 12/2019. The diagnosis of achalasia was established on the grounds of clinical symptoms, radiological and endoscopic findings, and esophageal manometry. Data on patient’s clinical characteristics, dilation technique and postprocedural follow-up were collected and statistically analyzed. Procedure effectiveness was defined as the postprocedural Eckardt score ≤ 3. Results: PD significantly reduced frequency of dysphagia, regurgitation, and retrosternal pain (p < 0.001). Body-weight increased significantly one month and one year after the procedure (p < 0.001). The procedural success rate was 100%. No severe complications were reported. Conclusions: PD is an effective and safe treatment modality in the management of achalasia. The study limitations include a single center design with the small number of participants, not all of whom underwent manometry, gender disproportion, absence of non-responders, and a short follow-up. Full article

Background: Lung cancer remains the leading cause of cancer-related mortality worldwide. Advances in screening, diagnosis, and management have transformed clinical practice, particularly with the integration of immunotherapy and target therapies. Methods: A systematic literature search was carried out for the period between October 2016 to September 2024. Phase II and III randomized trials evaluating ICI monotherapy, ICI–chemotherapy combinations, and dual ICI regimens in patients with advanced NSCLC were included. Outcomes of interest included overall survival (OS), progression-free survival (PFS), and treatment-related adverse events (AEs). Results: PD-1-targeted therapies demonstrated superior OS compared to PD-L1-based regimens, with cemiplimab monotherapyranking highest for OS benefit (posterior probability: 90%), followed by sintilimab plus platinum-based chemotherapy (PBC) and pemetrexed—PBC. PFS atezolizumab plus bevacizumab and PBC, and camrelizumab plus PBC were the most effective regimens. ICI–chemotherapy combinations achieved higher ORRs but were associated with greater toxicity. The most favorable safety profiles were observed with cemiplimab, nivolumab, and avelumab monotherapy, while atezolizumab plus PBC and sugemalimab plus PBC carried the highest toxicity burdens. Conclusions: In PD-L1-unselected advanced NSCLC, PD-1 blockade—particularly cemiplimab monotherapy—and rationally designed ICI–chemotherapy combinations represent the most efficacious treatment strategies. Balancing efficacy with safety remains critical, especially in the absence of predictive biomarkers. These findings support a patient-tailored approach to immunotherapy and highlight the need for further biomarker-driven and real-world investigations to optimize treatment selection. Full article

Background/Objectives: Neurodegenerative proteinopathies, such as Alzheimer’s disease (AD), Parkinson’s disease (PD), and dementia with Lewy bodies (DLB), are increasingly prevalent worldwide mainly due to population aging. These conditions are marked by complex etiologies, overlapping pathologies, and progressive clinical decline, with significant consequences for patients, caregivers, and healthcare systems. This review aims to synthesize evidence on the healthcare complexities of major neurodegenerative proteinopathies to highlight current knowledge gaps, and to inform future care models, policies, and research directions. Methods: We conducted a comprehensive literature search in PubMed/MEDLINE using combinations of MeSH terms and keywords related to neurodegenerative diseases, proteinopathies, diagnosis, sex, management, treatment, caregiver burden, and healthcare delivery. Studies were included if they addressed the clinical, pathophysiological, economic, or care-related complexities of aging-related neurodegenerative proteinopathies. Results: Key themes identified include the following: (1) multifactorial and unclear etiologies with frequent co-pathologies; (2) long prodromal phases with emerging biomarkers; (3) lack of effective disease-modifying therapies; (4) progressive nature requiring ongoing and individualized care; (5) high caregiver burden; (6) escalating healthcare and societal costs; and (7) the critical role of multidisciplinary and multi-domain care models involving specialists, primary care, and allied health professionals. Conclusions: The complexity and cost of neurodegenerative proteinopathies highlight the urgent need for prevention-focused strategies, innovative care models, early interventions, and integrated policies that support patients and caregivers. Prevention through the early identification of risk factors and prodromal signs is critical. Investing in research to develop effective disease-modifying therapies and improve early detection will be essential to reducing the long-term burden of these disorders. Full article

Background/Objectives: Brain metastasis (BM) is a common and often early manifestation in lung adenocarcinoma (LUAD), yet its tumor microenvironment remains poorly defined at the time of initial diagnosis. This study aims to characterize early immune microenvironmental alterations in synchronous BM using spatial proteomic profiling. Methods: We performed digital spatial proteomic profiling using the NanoString GeoMx platform on formalin-fixed paraffin-embedded tissues from five treatment-naïve LUAD patients in whom BM was the initial presenting lesion. Paired primary lung and brain metastatic samples were analyzed across tumor and stromal compartments using 68 immune- and tumor-related protein markers. Results: Spatial profiling revealed distinct expression patterns between primary tumors and brain metastases. Immune regulatory proteins—including IDO-1, PD-1, PD-L1, STAT3, PTEN, and CD44—were significantly reduced in brain metastases (p < 0.01), whereas pS6, a marker of activation-induced T-cell death, was significantly upregulated (p < 0.01). These alterations were observed in both tumor and stromal regions, suggesting a more immunosuppressive and apoptotic microenvironment in brain lesions. Conclusions: This study provides one of the first spatially resolved proteomic characterizations of synchronous BM at initial LUAD diagnosis. Our findings highlight early immune escape mechanisms and suggest the need for site-specific immunotherapeutic strategies in patients with brain metastasis. Full article

Objectives: The objective of this study was to assess the effect of aerobic exercise on depressive symptoms and to determine the optimal exercise prescription for Parkinson’s disease (PD) patients. Methods: A comprehensive search was conducted across PubMed, Web of Science, Cochrane, Scopus, and Embase databases. A meta-analysis was conducted to determine the standardized mean difference (SMD) and 95% confidence interval. Results: Aerobic exercise significantly alleviated depressive symptoms in PD patients (SMD, −0.68, p = 0.002). Subgroup analyses revealed that moderate intensity aerobic exercise (SMD, −0.72, p = 0.0006), interventions conducted for ≥12 weeks (SMD, −0.85, p = 0.04), ≥3 times per week (SMD, −0.68, p = 0.002), ≥60 min per session (SMD, −0.57, p < 0.0001), and ≥180 min per week (SMD, −0.87, p = 0.0002) were more effective in improving depressive symptoms in PD patients, especially in PD patients with a disease duration of ≤6 years (SMD, −1.00, p = 0.04). Conclusions: Integrating the available data, it is clear that aerobic exercise is a proven method for alleviating depressive symptoms in PD patients. This meta-analysis provides empirical support for clinicians to recommend that PD patients engage in aerobic exercise regimens of no less than 12 weeks’ duration, performed at a minimum frequency of three sessions per week, with each session lasting in excess of 60 min and a cumulative weekly duration of at least 180 min, to effectively attenuate depressive symptomatology. Earlier implementation of aerobic exercise interventions is recommended, as PD patients in the early stages of the disease (up to 6 years post-diagnosis) may derive the greatest benefit in terms of depression symptom improvement from such programs. Full article

Background: Epidemiological studies have reported an inverse association between smoking and Parkinson’s disease (PD) risk, prompting interest in nicotine as a potential therapeutic agent. The present meta-analysis evaluated the efficacy of nicotine therapy in improving motor symptoms and activities of daily living in patients with PD. Methods: PubMed, Embase, and Cochrane Library were systematically searched to identify randomized controlled trials (RCTs) assessing nicotine therapy in PD. Clinical RCTs administering interventions extending beyond 1 week and reporting motor or nonmotor outcomes were included. Random-effects models were used to analyze short-term (<6 months) and long-term (≥6 months) outcomes by using standardized mean differences (SMDs). Results: This meta-analysis included five RCTs (346 participants). Nicotine therapy led to no significant improvement in motor outcomes in the short term (pooled SMD: −0.452, 95% confidence interval: −1.612 to 0.708) or long term (pooled SMD: 0.174, 95% confidence interval: −0.438 to 0.787). Considerable interstudy heterogeneity was noted. Furthermore, short-term nicotine therapy resulted in no significant improvement in daily functioning, cognition, or quality of life. Conclusions: This meta-analysis revealed a lack of compelling evidence suggesting that nicotine-based therapies improve motor or nonmotor outcomes in PD. The findings highlight a disconnect between epidemiological associations and clinical efficacy. Given the prodromal nature of PD pathology and the challenges of early diagnosis, future preventive strategies should be implemented before symptom onset in high-risk individuals identified using advanced biomarker panels. Full article

Parkinson’s disease (PD) is a progressive neurological disorder with motor and non-motor symptoms that are inadequately addressed by current pharmacological and surgical therapies. Brain–computer interfaces (BCIs), particularly those based on electroencephalography (eBCIs), provide a promising, non-invasive approach to personalized neurorehabilitation. This narrative review explores the clinical potential of BCIs in PD, discussing signal acquisition, processing, and control paradigms. eBCIs are well-suited for PD due to their portability, safety, and real-time feedback capabilities. Emerging neurophysiological biomarkers—such as beta-band synchrony, phase–amplitude coupling, and altered alpha-band activity—may support adaptive therapies, including adaptive deep brain stimulation (aDBS), as well as motor and cognitive interventions. BCIs may also aid in diagnosis and personalized treatment by detecting these cortical and subcortical patterns associated with motor and cognitive dysfunction in PD. A structured search identified 11 studies involving 64 patients with PD who used BCIs for aDBS, neurofeedback, and cognitive rehabilitation, showing improvements in motor function, cognition, and engagement. Clinical translation requires attention to electrode design and user-centered interfaces. Ethical issues, including data privacy and equitable access, remain critical challenges. As wearable technologies and artificial intelligence evolve, BCIs could shift PD care from intermittent interventions to continuous, brain-responsive therapy, potentially improving patients’ quality of life and autonomy. This review highlights BCIs as a transformative tool in PD management, although more robust clinical evidence is needed. Full article

Parkinson’s disease (PD) is a neurodegenerative disorder marked by the progressive loss of dopaminergic neurons in the nigrostriatal pathway. The dopamine active transporter (DAT), a key protein involved in dopamine reuptake, serves as a selective biomarker for dopaminergic terminals in the striatum. DAT binding has been extensively studied using in vivo imaging techniques such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET). To support the design of new radiotracers targeting DAT, we employ Quantitative Structure–Activity Relationship (QSAR) analysis on a structurally diverse dataset composed of 57 compounds with known affinity constants for DAT. The best-performing QSAR model includes four molecular descriptors and demonstrates robust statistical performance: R² = 0.7554, Q²_LOO = 0.6800, and external R² = 0.7090. These values indicate strong predictive capability and model stability. The predicted compounds are evaluated using a docking methodology to check the correct coupling and interactions with the DAT. The proposed approach—combining QSAR modeling and docking—offers a valuable strategy for screening and optimizing potential PET/SPECT radiotracers, ultimately aiding in the neuroimaging and early diagnosis of Parkinson’s disease. Full article

In the wide range of human diseases, Parkinson’s disease (PD) has a high incidence, according to a recent survey by the World Health Organization (WHO). According to WHO records, this chronic disease has affected approximately 10 million people worldwide. Patients who do not receive an early diagnosis may develop an incurable neurological disorder. PD is a degenerative disorder of the brain, characterized by the impairment of the nigrostriatal system. A wide range of symptoms of motor and non-motor impairment accompanies this disorder. By using new technology, the PD is detected through speech signals of the PD victims by using the reduced instruction set computing 5th version (RISC-V) processor. The RISC-V microcontroller unit (MCU) was designed for the voice-controlled human-machine interface (HMI). With the help of signal processing and feature extraction methods, the digital signal is impaired by the impairment of the nigrostriatal system. These speech signals can be classified through classifier modules. A wide range of classifier modules are used to classify the speech signals as normal or abnormal to identify PD. We use Matrix Laboratory (MATLAB R2021a_v9.10.0.1602886) to analyze the data, develop algorithms, create modules, and develop the RISC-V processor for embedded implementation. Machine learning (ML) techniques are also used to extract features such as pitch, tremor, and Mel-frequency cepstral coefficients (MFCCs). Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that impairs motor function, including the fine motor control required for handwriting. Traditional diagnostic methods often lack sensitivity and objectivity in the early stages, limiting opportunities for timely intervention. There is a growing need for non-invasive, accessible tools capable of capturing subtle motor changes that precede overt clinical symptoms. Among early PD manifestations, handwriting impairments such as micrographia have shown potential as digital biomarkers. However, conventional handwriting analysis remains subjective and limited in scope. Recent advances in artificial intelligence (AI) and machine learning (ML) enable automated analysis of handwriting dynamics, such as pressure, velocity, and fluency, collected via digital tablets and smartpens. These tools support the detection of early-stage PD, monitoring of disease progression, and assessment of therapeutic response. This paper highlights how AI-enhanced handwriting analysis provides a scalable, non-invasive method to support diagnosis, enable remote symptom tracking, and personalize treatment strategies in PD. This approach integrates clinical neurology with computer science and rehabilitation, offering practical applications in telemedicine, digital health, and personalized medicine. By capturing dynamic features often missed by traditional assessments, AI-based handwriting analysis contributes to a paradigm shift in the early detection and long-term management of PD, with broad relevance across neurology, digital diagnostics, and public health innovation. Full article

Parkinson’s disease (PD) is the second most common neurodegenerative disorder in the world. PD is characterized by motor and non-motor symptoms, but the diagnosis primarily relies on the clinical assessment of postural and movement abnormalities, supported by imaging and genetic testing. It is widely accepted that the disease process begins decades before the onset of overt symptoms. Emerging evidence suggests that neuroinflammation plays a central role in the pathogenesis of PD, particularly during the pre-clinical phase. Activated microglia, increased levels of pro-inflammatory cytokines, and persistent oxidative stress have all been associated with the gradual loss of dopaminergic neurons. Although earlier detection and diagnosis remain elusive, achieving these goals is crucial for advancing prevention and disease-modifying strategies. Clinical studies are ongoing. To fill the gap, research models that recapitulate the chronic disease progression of PD are crucial to test preventive and disease-modifying strategies. This review briefly summarizes clinical knowledge on PD as a starting point for improving research models. Furthermore, we will critically evaluate how the existing models have been utilized and highlight opportunities to overcome their limitations and enhance the translational relevance to clinical application. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder marked by dopaminergic neuronal loss, α-synuclein aggregation, and chronic neuroinflammation. Recent evidence suggests that exosomal microRNAs (miRNAs)—small, non-coding RNAs encapsulated in extracellular vesicles—are key regulators of PD pathophysiology and promising candidates for biomarker development and therapeutic intervention. Exosomes facilitate intercellular communication, cross the blood–brain barrier, and protect miRNAs from degradation, rendering them suitable for non-invasive diagnostics and targeted delivery. Specific exosomal miRNAs modulate neuroinflammatory cascades, oxidative stress, and synaptic dysfunction, and their altered expression in cerebrospinal fluid and plasma correlates with disease onset, severity, and progression. Despite their translational promise, challenges persist, including methodological variability in exosome isolation, miRNA profiling, and delivery strategies. This review integrates findings from preclinical models, patient-derived samples, and systems biology to delineate the functional impact of exosomal miRNAs in PD. We propose mechanistic hypotheses linking miRNA dysregulation to molecular pathogenesis and present an interactome model highlighting therapeutic nodes. Advancing exosomal miRNA research may transform the clinical management of PD by enabling earlier diagnosis, molecular stratification, and the development of disease-modifying therapies. Full article

Background: The benefit of surgery for malignant pleural mesothelioma is highly debated, as few robust clinical trials show its effectiveness. Objective: To examine the long-term survival of patients with malignant pleural mesothelioma who underwent surgical treatment combined with neoadjuvant chemotherapy versus those who received chemotherapy alone. Methods: We analyzed a historical cohort of 122 patients diagnosed with mesothelioma, confirmed through histopathological examination. We compared the clinical and laboratory characteristics of the surgery and chemotherapy groups at baseline. We calculated Kaplan–Meier survival curves and used Cox’s proportional hazards model to evaluate the relationship between surgery and mortality. Results: Surgery was performed in 16 out of 122 cases. Pleurectomy/decortication (PD) represented 8 cases, while extrapleural pneumonectomy (EPP) accounted for the remaining 8 cases. At five years, survival rates for those who underwent surgery compared to chemotherapy alone were 53% (95% CI 15–81%) versus 23% (95% CI 10–40%), respectively. Survival among those who had PD was 67%, compared to 40% for those who had EPP. Surgical treatment was associated with improved survival, with a hazard ratio (HR) of 0.34 (95% CI 0.19–0.61) after adjusting for factors such as age over 65, the duration from symptom onset to diagnosis, hemoglobin levels below 10 g, a neutrophil-to-lymphocyte ratio over 6, and ECOG scores greater than 2. Conclusions: Mesothelioma surgery, whether it be PD or EPP, enhances patients’ survival compared to chemotherapy. PD produces better outcomes than EPP. Full article

Intravascular lymphoma (IVL) is a rare, aggressive subtype of non-Hodgkin lymphoma (NHL) characterized by the selective proliferation of neoplastic lymphoid cells within small and medium-sized blood vessels, most frequently of B-cell origin (IVLBCL). Its protean clinical presentation, lack of pathognomonic findings, and absence of tumor masses or lymphadenopathies often lead to diagnostic delays and poor outcomes. IVLBCL can manifest in classic, hemophagocytic syndrome-associated (HPS), or cutaneous variants, with extremely variable organ involvement including the central nervous system (CNS), skin, lungs, and endocrine system. Diagnosis requires histopathologic identification of neoplastic intravascular lymphoid cells via targeted or random tissue biopsies. Tumor cells are highly atypical and display a non-GCB B-cell phenotype, often expressing CD20, MUM1, BCL2, and MYC; molecularly, they frequently harbor mutations in MYD88 and CD79B, defining a molecular profile shared with ABC-type DLBCL of immune-privileged sites. Therapeutic approaches are based on rituximab-containing chemotherapy regimens (R-CHOP), often supplemented with CNS-directed therapy due to the disease’s marked neurotropism. Emerging strategies include autologous stem cell transplantation (ASCT) and novel immunotherapeutic approaches, potentially exploiting the frequent expression of PD-L1 by tumor cells. A distinct but related entity, intravascular NK/T-cell lymphoma (IVNKTCL), is an exceedingly rare EBV-associated lymphoma, showing unique own histologic, immunophenotypic, and molecular features and an even poorer outcome. This review provides a comprehensive overview of the current understandings about clinicopathological, molecular, and therapeutic landscape of IVL, emphasizing the need for increased clinical awareness, standardized diagnostic protocols, and individualized treatment strategies for this aggressive yet intriguing malignancy. Full article