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The incidence of heart-related illnesses is on the rise worldwide. Heart diseases are primarily caused by a multitude of parameters, including high blood pressure, diabetes, and excessive cholesterol, which are controlled by poor dietary and lifestyle choices. The growth in cardiovascular diseases (CVD) is mostly due to several other behaviors, such as smoking, drinking, and sleeplessness. In the research, machine learning-based prediction methods work on the audio recordings of heartbeats known as phonocardiograms (PCG) to develop an algorithm that differentiates a normal healthy heart from an abnormal heart based on the heart sounds. The data set consists of 831 normal and 260 abnormal data, and the duration of each sample is 5 seconds. Features extracted from the data are up-sampled and applied to the logistic regression and random forest classification models. The developed models record a classification accuracy of 71% for logistic regression and 94% for the random forest model. Further, artificial neural networks (ANN) and Deep learning networks have been trained to improve performance and demonstrated an accuracy of 94.5%.

Abstract Objective This study is aimed to investigate the causal relationship between plasma proteins and oral cancer risk using two-sample MR (Mendelian randomization). Methods Summary-level GWAS (genome-wide association study) data on plasma protein levels (4,907 proteins) and oral cancer (6,034 cases, 6,585 controls) of European ancestry were utilized. SNPs (single nucleotide polymorphisms) associated with proteins at genome-wide significance were selected as instrumental variables. Multiple MR methods including IVW (inverse-variance weighted), MR-Egger, weighted median, simple mode and weighted mode were applied to estimate causal effects. Sensitivity analyses were conducted. Results Eight plasma proteins (CCDC167, MID2, NDRG4, PEAR1, PIAS4, RCAN1, SAMHD1 and TNMD) were identified to have significant causal associations with oral cancer risk. NDRG4, RCAN1, SAMHD1 and TNMD were associated with increased oral cancer risk while PEAR1 was associated with decreased risk. The causal estimates were consistent across different methods. Sensitivity analyses indicated the results were robust without significant heterogeneity or horizontal pleiotropy. Multivariable MR adjusting for smoking, alcohol intake and periodontal disease showed CCDC167, MID2, NDRG4, PEAR1, PIAS4 and SAMHD1 still had direct effects on oral cancer. Conclusion This two-sample MR study provides evidence for potentially causal effects of several plasma proteins on oral cancer risk. The identified proteins may serve as biomarkers and shed light on biological mechanisms underlying oral carcinogenesis. Further research is warranted to validate and extend these findings.

This study elucidates how doctoral students perceive the challenges and impediments of their doctoral programs. In this study, the demands of doctoral programs are characterized as challenges that stimulate students’ potential and hindrances that threaten their well-being. Thirty-five full-time PhD students at various stages of their programme participated in semi-structured interviews as part of a qualitative study. The thematic analysis investigated students’ challenges and hindrances in their programs. Qualitative research identified three themes under challenge and hindrance demands: institutional, supervisory, and personal. The study developed six propositions based on transactional stress theory regarding challenge and hindrance demands. These propositions assist supervisors and doctorate educators in explaining the challenge and hindrance demands framework in a doctoral program context. Demands, such as ambiguity in doctoral research roles, inadequate resources for doctoral programs, and interpersonal conflict, threaten the well-being of doctoral students. Challenge demands such as workload, role responsibility, and role complexity support students’ potential growth and well-being. Furthermore, the present study offers three recommendations for enhancing the doctoral program: establishing an approachable institutional ecosystem, emphasizing the vital role of supervisors, and fostering work-life balance among students.

The Box and Block Test (BBT) is a reliable outcome measure in upper limb rehabilitation. The present work aims to automate the test of unilateral manual dexterity of a patient's hand. A box consisting of 80 blocks was divided into two partitions. A motion tracking sensor (MPU6050) was placed on a wrist to obtain the orientation of hand movement. Thirty-four healthy participants were chosen for the test. Each participant completed both traditional and automated versions of BBT within a one-week interval, with each session lasting 60 seconds. A colour sensor at the box monitored block movements across the partitions. A Liquid Crystal Display (LCD) and a 4-digit 7-segment display provided feedback on the number of cubes moved and timing information, respectively. An independent t-test revealed a non-significant difference between traditional BBT and automated BBT. Concurrent validity was established by correlating the results of the automated BBT with the traditional BBT. A positive correlation (r= 0.94) was found between traditional and automated BBT. This validates the automated BBT report with the same result as traditional BBT. Automated BBT can assist a broad range of patients in independently tracking the progress of their hand injuries or conditions without needing a clinician. This can be conveniently done from the comfort of their homes. HighlightsLack of fine motor skills and dexterity is a primary concern affecting the person’s daily living activity; hence, the automated box and block test (BBT) is valid for assessing hand dexterity quantitatively.The traditional BBT is an old and golden test used in dexterity assessment with neurological patients, which only provides outcomes in the form of box count, which is insufficient for hand assessment.The need for this work arises from the limitations of the traditional BBT, which only provides a basic outcome in the form of box counts, insufficient for comprehensive hand dexterity assessment, particularly in neurological patients.Automated BBT is developed, which reduces the clinician’s burden during assessment and makes the test foolproof by adding a mechanism to capture hand trajectory during block movements.Along with the block count, the accelerometer gives hand orientation during the test process, which helps the clinician further analyze the results, such as motion pattern analysis, temporal analysis and comparative analysis.The independent t-test between traditional BBT and automated BBT, p = 0.109, was observed with a 0.95 confidence interval. Levene’s test revealed no significant difference between the scores of the traditional and automated BBT (t = 2.03; p = 0.109), also between dominant and non-dominant hand (t = 1.99; p = 0.74). A positive Pearson’s correlation (r = 0.94, p

IntroductionAn immobile patient cannot be weighed on a stand-on weighing machine, i.e., a bathroom scale. They have to get weighed while lying, which is not easy. The main objective of this research is to design a medical apparatus that measures the patient's weight in a lying condition. To achieve this the apparatus is designed as a stretcher to carry the patient in and around the hospital.MethodsThe stretcher has four load cells to measure the patient's weight; it can bear a weight of 500 kg and has a self-weight of 20 kg. A Microcontroller unit (MCU) is embedded into the apparatus to weigh the patient lying on it. The stretcher comprises the top frame, middle frame, and base frame. The top frame can be detached and mounted back to the middle frame; this will help the medical personnel shift the patients from a medical bed. The middle frame is a plate structure where the four load cells are mounted at the corners of the lower plate. The upper plate functions as a pressure plate on the load cell. The base plate has four heavy-duty wheels that can bear the load. The middle frame and base frame, together, form a single structure, giving mobility to the structure. A control panel is employed with reset, tare, and on-off buttons to control the embedded platform. The LCD panel on the side of the apparatus shows the weight when the patient is placed on top of the apparatus.Results and discussionA prototype trolley equipped with a wireless data logging system was tested on 10 healthy participants. The device accurately measured weight within ±50 g across a scale range of 2–140 kg, with data captured every 30 s over a 5-min testing period. Wireless communication was successfully demonstrated over a 100-m range. The important add-on feature of this work is the apparatus is connected to the internet, transforming it into an IoT-based medical device.

Abstract Objectives The purpose of this umbrella review was to gather and summarize the data from published systematic reviews (SRs) that compared non-surgical mechanical debridement (NSMD) with and without the use of adjunctive treatments on the management of peri-implant mucositis (PIM). Materials and methods A protocol was developed and registered in PROSPERO (CRD42021254350) before the systematic search for the SRs. Seven electronic databases, including Cochrane Library, Embase (via Ovid), MEDLINE (via Pubmed), Proquest, Prospero, Scopus and Web of Science, were searched for published reviews. The search for unpublished and informally published reviews was further attempted in the last four databases. The methodological quality of the included reviews was assessed using AMSTAR 2. Results Twelve included SRs assessed clinical studies published between 2014 and 2020, including a total of seventeen primary clinical trials. All SRs summarized data from individual studies and provided a narrative conclusion regarding the effectiveness of the adjunctive treatments. Only six SRs performed a meta-analysis (MA) of additional benefits of the adjunctive therapy for PIM, with results indicating no significant difference between the different treatment modalities. The overall confidence was adjudged ranging from critically low to low using AMSTAR 2 and significant additional benefits of any adjunctive treatments in comparison with NSMD were not apparent. Conclusion Overall, the reviewed evidence did not support the use of adjunctive treatments for improvement of clinical outcomes in PM management as compared to NSMD alone.

Dexterity is the coordination of hands and fingers with the eyes. Existing unilateral movements and dexterity tests use wearable devices for recording hand assessment. It is understandable that whenever some wired devices tag hands, the patient tends to be more conscious, which may not reflect the actual outcome. In this paper, using the finger-tap counter and loop-wire test, an attempt is made to evaluate unilateral movements and dexterity in adults of different age groups without using any wearable device. The test timings are recorded automatically with the help of the ATmega328p microcontroller and transferred to the clinician wirelessly via Bluetooth communication. Healthy 30 subjects between the age range (20–35, 36–50, 51–65, 66–80) were tested for finger-tap counter and loop-wire tests. The experiment is also tested for dominant and non-dominant hands for the same subjects. In this experiment, the result shows as the age increases, the trend in the assessment completion time and number of failures during the performance of the test increases. Female groups can perform both tests quicker than male groups with fewer errors irrespective of age group.

AimThis study aims to identify pyroptosis-related genes (PRGs), their functional immune characteristics, and distinct pyroptosis-related clusters in periodontitis.MethodsDifferentially expressed (DE)-PRGs were determined by merging the expression profiles of GSE10334, GSE16134, and PRGs obtained from previous literatures and Molecular Signatures Database (MSigDB). Least absolute shrinkage and selection operator (LASSO) regression was applied to screen the prognostic PRGs and develop a prognostic model. Consensus clustering was applied to determine the pyroptosis-related clusters. Functional analysis and single-sample gene set enrichment analysis (ssGSEA) were performed to explore the biological characteristics and immune activities of the clusters. The hub pyroptosis-related modules were defined using weighted correlation network analysis (WGCNA).ResultsOf the 26 periodontitis-related DE-PRGs, the highest positive relevance was for High-Mobility Group Box 1 (HMGB1) and SR-Related CTD Associated Factor 11 (SCAF11). A 14-PRG-based signature was developed through the LASSO model. In addition, three pyroptosis-related clusters were obtained based on the 14 prognostic PRGs. Caspase 3 (CASP3), Granzyme B (GZMB), Interleukin 1 Alpha (IL1A), IL1Beta (B), IL6, Phospholipase C Gamma 1 (PLCG1) and PYD And CARD Domain Containing (PYCARD) were dysregulated in the three clusters. Distinct biological functions and immune activities, including human leukocyte antigen (HLA) gene expression, immune cell infiltration, and immune pathway activities, were identified in the three pyroptosis-related clusters of periodontitis. Furthermore, the pink module associated with endoplasmic stress-related functions was found to be correlated with cluster 2 and was suggested as the hub pyroptosis-related module.ConclusionThe study identified 14 key pyroptosis-related genes, three distinct pyroptosis-related clusters, and one pyroptosis-related gene module describing several molecular aspects of pyroptosis in the pathogenesis and immune micro-environment regulation of periodontitis and also highlighted functional heterogeneity in pyroptosis-related mechanisms.

Background: This systematic review evaluates the oral health-related quality of life (OHRQoL) of patients with chronic respiratory diseases.Methods: A systematic literature search was performed based on the PubMed, Medline, Web of Science, and Scopus, using the search terms: “oral health-related quality of life” and “respiratory disease” or “lung” and “oral health-related quality of life.” Full-text articles published until June 30, 2021 and reporting any OHRQoL measurement in children or adults with a chronic respiratory disease or condition were included and analyzed qualitatively.Results: A total of seven out of 44 studies were included, of which four studies examined adults and three studies investigated children. The respective diseases were chronic obstructive pulmonary disease (COPD) (n = 2), sleep apnea (n = 2), severe asthma (n = 1), cystic fibrosis (n = 1), and lung transplantation (n = 1). Four studies confirmed a worse OHRQoL in the respiratory diseased group compared to healthy controls. The overall OHRQoL was reduced in the included studies. Oral health, health-related quality of life, and disease-related parameters were rarely examined with regard to OHRQoL.Conclusion: Patients with chronic respiratory diseases show a reduced OHRQoL. Oral health should be fostered in these individuals to support their OHRQoL.

ObjectiveThis study aimed to identify the programmed death ligand-1 (PDL1, also termed as CD274) and its positively correlated immune checkpoint genes (ICGs) and to determine the immune subtypes of CD274-centered ICG combinations in oral and squamous cell carcinoma (OSCC).Materials and MethodsFirstly, the 95 ICGs obtained via literature reviews were identified in the Cancer Genome Atlas (TCGA) database in relation to OSCC, and such 88 ICG expression profiles were extracted. ICGs positively correlated with CD274 were utilized for subsequent analysis. The relationship between ICGs positively correlated with CD274 and immunotherapy biomarkers (tumor mutation burden (TMB), and adaptive immune resistance pathway genes) was investigated, and the relationships of these genes with OSCC clinical features were explored. The prognostic values of CD274 and its positively correlated ICGs and also their associated gene pairs were revealed using the survival analysis.ResultsEight ICGs, including CTLA4, ICOS, TNFRSF4, CD27, B- and T-lymphocyte attenuator (BTLA), ADORA2A, CD40LG, and CD28, were found to be positively correlated with CD274. Among the eight ICGs, seven ICGs (CTLA4, ICOS, TNFRSF4, CD27, BTLA, CD40LG, and CD28) were significantly negatively correlated with TMB. The majority of the adaptive immune resistance pathway genes were positively correlated with ICGs positively correlated with CD274. The survival analysis utilizing the TCGA-OSCC data showed that, although CD274 was not significantly associated with overall survival (OS), the majority of ICGs positively correlated with CD274 (BTLA, CD27, CTLA4, CD40LG, CD28, ICOS, and TNFRSF4) were significantly correlated with OS, whereby their low-expression predicted a favorable prognosis. The survival analysis based on the gene pair subtypes showed that the combination subtypes of CD274_low/BTLA_low, CD274_low/CD27_low, CD274_low/CTLA4_low, CD8A_high/BTLA_low, CD8A_high/CD27_low, and CD8A_high/CTLA4_low predicted favorable OS.ConclusionThe results in this study provide a theoretical basis for prognostic immune subtyping of OSCC and highlight the importance of developing future immunotherapeutic strategies for treating oral cancer.

This study aimed to assess the accuracy of diagnosis of vertical furcation subclass in molars using periapical radiographs (PAs) and clinical chartings compared against cone-beam computer tomography (CBCT) as the gold standard. The protocol involved examiners with different levels of experience. This retrospective radiographic study retrieved 40 molar teeth with full periodontal chartings, PAs, and CBCT records. Fifteen examiners with different levels of experience evaluated the PAs and periodontal chartings to assess the vertical depth of furcation and, thus, the vertical subclassification. CBCT was used as the gold standard for comparison. The accuracy of vertical furcal depth measured was assessed together with the accuracy of vertical subclassification assignment. The reliability of the conventional diagnostic modality among the examiners was also evaluated. A linear mixed model adjusted for the CBCT vertical furcal depth measurement was constructed to determine if tooth position, horizontal furcation distribution, and examiner experience level affect the bias in the vertical depth of furcation measurement. The reliability of the conventional periodontal diagnostic method in measuring vertical furcal depth was found to be fair, while vertical subclass assignment was moderate. Significantly better reliability during subclass assignment was found with mandibular molars (p < 0.001) and in maxillary molars with isolated buccal class II furcation. Within the study’s limitations, conventional periodontal diagnostics based on periapical radiographs and clinical periodontal chartings appear to be in poor to fair agreement with CBCT (gold standard) when measuring the vertical depth of furcation. Examiners with the least experience were more prone to bias when estimating the vertical furcal depth.

Background: The mechanisms through which immunosuppressed patients bear increased risk and worse survival in oral squamous cell carcinoma (OSCC) are unclear. Here, we used deep learning to investigate the genetic mechanisms underlying immunosuppression in the survival of OSCC patients, especially from the aspect of various survival-related subtypes.Materials and methods: OSCC samples data were obtained from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and OSCC-related genetic datasets with survival data in the National Center for Biotechnology Information (NCBI). Immunosuppression genes (ISGs) were obtained from the HisgAtlas and DisGeNET databases. Survival analyses were performed to identify the ISGs with significant prognostic values in OSCC. A deep learning (DL)-based model was established for robustly differentiating the survival subpopulations of OSCC samples. In order to understand the characteristics of the different survival-risk subtypes of OSCC samples, differential expression analysis and functional enrichment analysis were performed.Results: A total of 317 OSCC samples were divided into one inferring cohort (TCGA) and four confirmation cohorts (ICGC set, GSE41613, GSE42743, and GSE75538). Eleven ISGs (i.e., BGLAP, CALCA, CTLA4, CXCL8, FGFR3, HPRT1, IL22, ORMDL3, TLR3, SPHK1, and INHBB) showed prognostic value in OSCC. The DL-based model provided two optimal subgroups of TCGA-OSCC samples with significant differences (p = 4.91E-22) and good model fitness [concordance index (C-index) = 0.77]. The DL model was validated by using four external confirmation cohorts: ICGC cohort (n = 40, C-index = 0.39), GSE41613 dataset (n = 97, C-index = 0.86), GSE42743 dataset (n = 71, C-index = 0.87), and GSE75538 dataset (n = 14, C-index = 0.48). Importantly, subtype Sub1 demonstrated a lower probability of survival and thus a more aggressive nature compared with subtype Sub2. ISGs in subtype Sub1 were enriched in the tumor-infiltrating immune cells-related pathways and cancer progression-related pathways, while those in subtype Sub2 were enriched in the metabolism-related pathways.Conclusion: The two survival subtypes of OSCC identified by deep learning can benefit clinical practitioners to divide immunocompromised patients with oral cancer into two subpopulations and give them target drugs and thus might be helpful for improving the survival of these patients and providing novel therapeutic strategies in the precision medicine area.

Abstract Background Past meta-analyses have shown adjunctive systemic Azithromycin (AZI) to provide minor clinical benefits in scaling and root surface debridement (S/RSD). However, these have not considered the covariance of key outcome parameters; probing pocket depth (PPD) and Clinical Attachment Level (CAL) or systematically examined some potential sources of heterogeneity. Aim To jointly synthesize 6-month outcomes of systemic AZI as adjunctive to S/RSD in chronic periodontitis and investigate 3 potential sources of heterogeneity. Methods Four databases were searched for suitable randomized controlled clinical trials (RCTs). Standardized mean differences (SMD) in PPD and CAL between AZI + S/RSD and S/RSD alone, at 6-month follow-up were computed. Within-study covariances of PPD and CAL were derived from reported multiple time-point data. A multivariate meta-analysis with random effects jointly modelled PPD and CAL, factoring in their covariance. This model included 3 moderators with interaction effects; timing of AZI initiation (pre-therapy/post-therapy), type of S/RSD [full-mouth debridement (FMD)/partial-mouth debridement (PMD)], and baseline study-level mean values of PPD/CAL. Results Among 276 abstracts, 11 observations from 9 RCTs qualified for meta-analysis. Within-study correlation-coefficients of PPD with CAL significantly increased with increasing study-level baseline mean values (Spearman’s r = 0.79, p

BackgroundPeriodontitis is a chronic immuno-inflammatory disease characterized by inflammatory destruction of tooth-supporting tissues. Its pathogenesis involves a dysregulated local host immune response that is ineffective in combating microbial challenges. An integrated investigation of genes involved in mediating immune response suppression in periodontitis, based on multiple studies, can reveal genes pivotal to periodontitis pathogenesis. Here, we aimed to apply a deep learning (DL)-based autoencoder (AE) for predicting immunosuppression genes involved in periodontitis by integrating multiples omics datasets.MethodsTwo periodontitis-related GEO transcriptomic datasets (GSE16134 and GSE10334) and immunosuppression genes identified from DisGeNET and HisgAtlas were included. Immunosuppression genes related to periodontitis in GSE16134 were used as input to build an AE, to identify the top disease-representative immunosuppression gene features. Using K-means clustering and ANOVA, immune subtype labels were assigned to disease samples and a support vector machine (SVM) classifier was constructed. This classifier was applied to a validation set (Immunosuppression genes related to periodontitis in GSE10334) for predicting sample labels, evaluating the accuracy of the AE. In addition, differentially expressed genes (DEGs), signaling pathways, and transcription factors (TFs) involved in immunosuppression and periodontitis were determined with an array of bioinformatics analysis. Shared DEGs common to DEGs differentiating periodontitis from controls and those differentiating the immune subtypes were considered as the key immunosuppression genes in periodontitis.ResultsWe produced representative molecular features and identified two immune subtypes in periodontitis using an AE. Two subtypes were also predicted in the validation set with the SVM classifier. Three “master” immunosuppression genes, PECAM1, FCGR3A, and FOS were identified as candidates pivotal to immunosuppressive mechanisms in periodontitis. Six transcription factors, NFKB1, FOS, JUN, HIF1A, STAT5B, and STAT4, were identified as central to the TFs-DEGs interaction network. The two immune subtypes were distinct in terms of their regulating pathways.ConclusionThis study applied a DL-based AE for the first time to identify immune subtypes of periodontitis and pivotal immunosuppression genes that discriminated periodontitis from the healthy. Key signaling pathways and TF-target DEGs that putatively mediate immune suppression in periodontitis were identified. PECAM1, FCGR3A, and FOS emerged as high-value biomarkers and candidate therapeutic targets for periodontitis.

Objective: To profile the salivary microbiomes of a Hong Kong Chinese cohort at a species-level resolution and determine species that discriminated clinically resolved periodontitis from periodontally healthy cases.Methods: Salivary microbiomes of 35 Hong Kong Chinese subjects' under routine supportive dental care were analyzed. All subjects had been treated for any dental caries or periodontal disease with all restorative treatment completed at least 1 year ago and had ≤3 residual pockets. They were categorized based on a past diagnosis of chronic periodontitis into “healthy” (H) or “periodontitis” (P) categories. Unstimulated whole saliva was collected, genomic DNA was isolated, and high throughput Illumina MiSeq sequencing of 16S rRNA (V3-V4) gene amplicons was performed. The sequences were assigned taxonomy at the species level by using a BLASTN based algorithm that used a combined reference database of HOMD RefSeqV14.51, HOMD RefSeqExtended V1.1 and GreenGeneGold. Species-level OTUs were subjected to downstream analysis in QIIME and R. For P and H group comparisons, community diversity measures were compared, differentially abundant species were determined using DESeq2, and disease indicator species were determined using multi-level pattern analysis within the R package “indicspecies.”Results: P subjects were significantly older than H subjects (p = 0.003) but not significantly different in their BOP scores (p = 0.82). No significant differences were noted in alpha diversity measures after adjusting for age, gender, and BOP or in the beta diversity estimates. Four species; Treponema sp. oral taxon 237, TM7 sp. Oral Taxon A56, Prevotella sp. oral taxon 314, Prevotella sp. oral taxon 304, and Capnocytophaga leadbetteri were significantly more abundant in P than in the H group. Indicator species analysis showed 7 significant indicators species of P group. Fusobacterium sp oral taxon 370 was the sole positive indicator of P group (positive predictive value = 0.9, p = 0.04). Significant indicators of the H category were Leptotrichia buccalis, Corynebacterium matruchotii, Leptotrichia hofstadii, and Streptococcus intermedius.Conclusion: This exploratory study showed salivary microbial species could discriminate treated, well-maintained chronic periodontitis from healthy controls with similar gingival inflammation levels. The findings suggest that certain salivary microbiome features may identify periodontitis-susceptible individuals despite clinical disease resolution.

Aims To explore molecular mechanisms that link peri-implantitis and type 2 diabetes mellitus (T2DM) by bioinformatic analysis of publicly available experimental transcriptomic data. Materials and methods Gene expression data from peri-implantitis were downloaded from the Gene Expression Omnibus database, integrated and differentially expressed genes (DEGs) in peri-implantitis were identified. Next, experimentally validated and computationally predicted genes related to T2DM were downloaded from the DisGeNET database. Protein–protein interaction network (PPI) pairs of DEGs related to peri-implantitis and T2DM related genes were constructed, “hub” genes and overlapping DEG were determined. Functional enrichment analysis was used to identify significant shared biological processes and signaling pathways. The PPI networks were subjected to cluster and specific class analysis for identifying “leader” genes. Module network analysis of the merged PPI network identified common or cross-talk genes connecting the two networks. Results A total of 92 DEGs overlapped between peri-implantitis and T2DM datasets. Three hub genes (IL-6, NFKB1, and PIK3CG) had the highest degree in PPI networks of both peri-implantitis and T2DM. Three leader genes (PSMD10, SOS1, WASF3), eight cross-talk genes (PSMD10, PSMD6, EIF2S1, GSTP1, DNAJC3, SEC61A1, MAPT, and NME1), and one signaling pathway (IL-17 signaling) emerged as peri-implantitis and T2DM linkage mechanisms. Conclusions Exploration of available transcriptomic datasets revealed IL-6, NFKB1, and PIK3CG expression along with the IL-17 signaling pathway as top candidate molecular linkage mechanisms between peri-implantitis and T2DM.

Platelet Rich Plasma (PRP) has been combined with autologous bone or bone substitutes and used for periodontal regeneration. The clinical efficacy of this combination has been noted. However, it remains questionable whether this clinical efficacy is due to the PRP or the bone graft material alone with which PRP is used in combination. Objective was to assess the clinical effectiveness of autologous PRP alone in treating intrabony defects in humans. 6 patients were recruited having interproximal intrabony defects with pocket probing depth ≥ 5 mm at re-evaluation following initial therapy, and angular osseous defect depth ≥ 4mm as assessed by using Spiral Computed Tomography. Autologous PRP gel was obtained by mixing autologous PRP with autologous thrombin and placed in the intrabony defects after defect debridement. At 6 months postoperatively, mean probing pocket depth (PPD) noted at baseline (7± 1.27)mm reduced to3.67± 1.03 mm, clinical attachment level (CAL) gain was 3.33± 0.51mm, both of which were statistically significant. Mean distance from cemento-enameljunction (CEJ) to base of the defect (BOD) at baseline was 8.55 ± 0.89 mm. At 6 months it reduced to 6.58 ±1.13 mm showing a defectfillof 1.96±00.32 mm, which was statistically significant. Thepercentage of defect fill noted was 23.01 % and mean defect resolution was 2.05 ±0.30 mm, both of which were statistically significant. Treatment of intrabony defects by autologous PRP gel alone caused significant soft tissue clinical improvement as well as hard tissue defect fill as evidenced by SSD view in spiral computed tomography.

Introduction: An immobile patient cannot be weighed on a stand-on weighing machine, i.e., a bathroom scale. They have to get weighed while lying, which is not easy. The main objective of this research is to design a medical apparatus that measures the patient's weight in a lying condition. To achieve this the apparatus is designed as a stretcher to carry the patient in and around the hospital. Methods: The stretcher has four load cells to measure the patient's weight; it can bear a weight of 500 kg and has a self-weight of 20 kg. A Microcontroller unit (MCU) is embedded into the apparatus to weigh the patient lying on it. The stretcher comprises the top frame, middle frame, and base frame. The top frame can be detached and mounted back to the middle frame; this will help the medical personnel shift the patients from a medical bed. The middle frame is a plate structure where the four load cells are mounted at the corners of the lower plate. The upper plate functions as a pressure plate on the load cell. The base plate has four heavy-duty wheels that can bear the load. The middle frame and base frame, together, form a single structure, giving mobility to the structure. A control panel is employed with reset, tare, and on-off buttons to control the embedded platform. The LCD panel on the side of the apparatus shows the weight when the patient is placed on top of the apparatus. Results and discussion: A prototype trolley equipped with a wireless data logging system was tested on 10 healthy participants. The device accurately measured weight within ±50 g across a scale range of 2-140 kg, with data captured every 30 s over a 5-min testing period. Wireless communication was successfully demonstrated over a 100-m range. The important add-on feature of this work is the apparatus is connected to the internet, transforming it into an IoT-based medical device. [ABSTRACT FROM AUTHOR]

Polycystic ovary syndrome (PCOS) requires early and precise diagnosis to manage and prevent long-term health consequences effectively. In this research, a large dataset of healthcare data gathered from various hospitals in Kerala, India, was evaluated using multiple machine learning (ML) and deep learning (DL) models to identify a highly reliable and accurate prediction of PCOS. The six algorithms used for comparison with the proposed DL model are support vector classification, random forest, logistic regression, k-nearest neighbors, and gaussian naive Bayes; they were selected due to their strengths in handling features in large datasets. The highly parameterized neural networks were tuned using efficient approaches like Optuna and genetic algorithms. The results indicated that the model implemented using our proposed combination of DL model and Optuna, outperformed the traditional models, achieving 93.55% reliability. This suggests the potential for using deep learning for decision-making in diagnosing PCOS. This method demonstrates the importance of integrating various data types with powerful analytic tools in medical diagnostics to support customized therapy. [ABSTRACT FROM AUTHOR]