Exploring the Impact of Hand Dominance on Laparoscopic Surgical Skills Development Using Network Models.

BACKGROUND: Laparoscopic surgery demands high precision and skill, necessitating effective training protocols that account for factors such as hand dominance. This study investigates the impact of hand dominance on the acquisition and proficiency of laparoscopic surgical skills, utilizing a novel assessment method that combines Network Models and electromyography (EMG) data. METHODS: Eighteen participants, comprising both medical and non-medical students, engaged in laparoscopic simulation tasks, including peg transfer and wire loop tasks. Performance was assessed using Network Models to analyze EMG data, capturing muscle activity and learning progression. The NASA Task Load Index (TLX) was employed to evaluate subjective task demands and workload perceptions. RESULTS: Our analysis revealed significant differences in learning progression and skill proficiency between dominant and non-dominant hands, suggesting the need for tailored training approaches. Network Models effectively identified patterns of skill acquisition, while NASA-TLX scores correlated with participants' performance and learning progression, highlighting the importance of considering both objective and subjective measures in surgical training. CONCLUSIONS: The study underscores the importance of hand dominance in laparoscopic surgical training and suggests that personalized training protocols could enhance surgical precision, efficiency, and patient outcomes. By leveraging advanced analytical techniques, including Network Models and EMG data analysis, this research contributes to optimizing clinical training methodologies, potentially revolutionizing surgical education and improving patient care.

Bacteria commonly associated with central nervous system catheter infections elicit distinct CSF proteome signatures.

Background: Cerebrospinal fluid (CSF) shunt infection is a common and devastating complication of the treatment of hydrocephalus. Timely and accurate diagnosis is essential as these infections can lead to long-term neurologic consequences including seizures, decreased intelligence quotient (IQ) and impaired school performance in children. Currently the diagnosis of shunt infection relies on bacterial culture; however, culture is not always accurate since these infections are frequently caused by bacteria capable of forming biofilms, such as Staphylococcus epidermidis, Cutibacterium acnes, and Pseudomonas aeruginosa resulting in few planktonic bacteria detectable in the CSF. Therefore, there is a critical need to identify a new rapid, and accurate method for diagnosis of CSF shunt infection with broad bacterial species coverage to improve the long-term outcomes of children suffering from these infections. Methods: To investigate potential biomarkers that would discriminate S. epidermidis, C. acnes and P. aeruginosa central nervous system (CNS) catheter infection we leveraged our previously published rat model of CNS catheter infection to perform serial CSF sampling to characterize the CSF proteome during these infections compared to sterile catheter placement. Results: P. aeruginosa infection demonstrated a far greater number of differentially expressed proteins when compared to S. epidermidis and C. acnes infection and sterile catheters, and these changes persisted throughout the 56-day time course. S. epidermidis demonstrated an intermediate number of differentially expressed proteins, primarily at early time points that dissipated over the course of infection. C. acnes induced the least amount of change in the CSF proteome when compared to the other pathogens. Conclusions: Despite the differences in the CSF proteome with each organism compared to sterile injury, several proteins were common across all bacterial species, especially at day 5 post-infection, which are candidate diagnostic biomarkers.

People With Human Immunodeficiency Virus Receiving Suppressive Antiretroviral Therapy Show Typical Antibody Durability After Dual Coronavirus Disease 2019 Vaccination and Strong Third Dose Responses.

BACKGROUND: Longer-term humoral responses to 2-dose coronavirus disease 2019 (COVID-19) vaccines remain incompletely characterized in people living with human immunodeficiency virus (HIV) (PLWH), as do initial responses to a third dose. METHODS: We measured antibodies against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein receptor-binding domain, angiotensin-converting enzyme 2 (ACE2) displacement, and viral neutralization against wild-type and Omicron strains up to 6 months after 2-dose vaccination, and 1 month after the third dose, in 99 PLWH receiving suppressive antiretroviral therapy and 152 controls. RESULTS: Although humoral responses naturally decline after 2-dose vaccination, we found no evidence of lower antibody concentrations or faster rates of antibody decline in PLWH compared with controls after accounting for sociodemographic, health, and vaccine-related factors. We also found no evidence of poorer viral neutralization in PLWH after 2 doses, nor evidence that a low nadir CD4+ T-cell count compromised responses. Post-third-dose humoral responses substantially exceeded post-second-dose levels, though Omicron-specific responses were consistently weaker than responses against wild-type virus. Nevertheless, post-third-dose responses in PLWH were comparable to or higher than controls. An mRNA-1273 third dose was the strongest consistent correlate of higher post-third-dose responses. CONCLUSION: PLWH receiving suppressive antiretroviral therapy mount strong antibody responses after 2- and 3-dose COVID-19 vaccination. Results underscore the immune benefits of third doses in light of Omicron.

Detection of Parkinson's Disease Using Wrist Accelerometer Data and Passive Monitoring.

Parkinson's disease is a neurodegenerative disorder impacting patients' movement, causing a variety of movement abnormalities. It has been the focus of research studies for early detection based on wearable technologies. The benefit of wearable technologies in the domain rises by continuous monitoring of this population's movement patterns over time. The ubiquity of wrist-worn accelerometry and the fact that the wrist is the most common and acceptable body location to wear the accelerometer for continuous monitoring suggests that wrist-worn accelerometers are the best choice for early detection of the disease and also tracking the severity of it over time. In this study, we use a dataset consisting of one-week wrist-worn accelerometry data collected from individuals with Parkinson's disease and healthy elderlies for early detection of the disease. Two feature engineering methods, including epoch-based statistical feature engineering and the document-of-words method, were used. Using various machine learning classifiers, the impact of different windowing strategies, using the document-of-words method versus the statistical method, and the amount of data in terms of number of days were investigated. Based on our results, PD was detected with the highest average accuracy value (85% ± 15%) across 100 runs of SVM classifier using a set of features containing features from every and all windowing strategies. We also found that the document-of-words method significantly improves the classification performance compared to the statistical feature engineering model. Although the best performance of the classification task between PD and healthy elderlies was obtained using seven days of data collection, the results indicated that with three days of data collection, we can reach a classification performance that is not significantly different from a model built using seven days of data collection.

Role of Staphylococcus aureus Formate Metabolism during Prosthetic Joint Infection.

Biofilms are bacterial communities characterized by antibiotic tolerance. Staphylococcus aureus is a leading cause of biofilm infections on medical devices, including prosthetic joints, which represent a significant health care burden. The major leukocyte infiltrate associated with S. aureus prosthetic joint infection (PJI) is granulocytic myeloid-derived suppressor cells (G-MDSCs), which produce IL-10 to promote biofilm persistence by inhibiting monocyte and macrophage proinflammatory activity. To determine how S. aureus biofilm responds to G-MDSCs and macrophages, biofilms were cocultured with either leukocyte population followed by RNA sequencing. Several genes involved in fermentative pathways were significantly upregulated in S. aureus biofilm following G-MDSC coculture, including formate acetyltransferase (pflB), which catalyzes the conversion of pyruvate and coenzyme-A into formate and acetyl-CoA. A S. aureus pflB mutant (ΔpflB) did not exhibit growth defects in vitro. However, ΔpflB formed taller and more diffuse biofilm compared to the wild-type strain as revealed by confocal microscopy. In a mouse model of PJI, the bacterial burden was significantly reduced with ΔpflB during later stages of infection, which coincided with decreased G-MDSC influx and increased neutrophil recruitment, and ΔpflB was more susceptible to macrophage killing. Although formate was significantly reduced in the soft tissue surrounding the joint of ΔpflB-infected mice levels were increased in the femur, suggesting that host-derived formate may also influence bacterial survival. This was supported by the finding that a ΔpflBΔfdh strain defective in formate production and catabolism displayed a similar phenotype to ΔpflB. These results revealed that S. aureus formate metabolism is important for promoting biofilm persistence.

Tumor-Derived Extracellular Vesicles Predict Clinical Outcomes in Oligometastatic Prostate Cancer and Suppress Antitumor Immunity.

PURPOSE: SABR has demonstrated clinical benefit in oligometastatic prostate cancer. However, the risk of developing new distant metastatic lesions remains high, and only a minority of patients experience durable progression-free response. Therefore, there is a critical need to identify which patients will benefit from SABR alone versus combination SABR and systemic agents. Herein we provide, to our knowledge, the first proof-of-concept of circulating prostate cancer-specific extracellular vesicles (PCEVs) as a noninvasive predictor of outcomes in oligometastatic castration-resistant prostate cancer (omCRPC) treated with SABR. METHODS AND MATERIALS: We analyzed the levels and kinetics of PCEVs in the peripheral blood of 79 patients with omCRPC at baseline and days 1, 7, and 14 after SABR using nanoscale flow cytometry and compared with baseline values from cohorts with localized and widely metastatic prostate cancer. The association of omCRPC PCEV levels with oncological outcomes was determined with Cox regression models. RESULTS: Levels of PCEVs were highest in mCRPC followed by omCRPC and were lowest in localized prostate cancer. High PCEV levels at baseline predicted a shorter median time to distant recurrence (3.5 vs 6.6 months; P = .0087). After SABR, PCEV levels peaked on day 7, and median overall survival was significantly longer in patients with elevated PCEV levels (32.7 vs 27.6 months; P = .003). This suggests that pretreatment PCEV levels reflect tumor burden, whereas early changes in PCEV levels after treatment predict response to SABR. In contrast, radiomic features of 11C-choline positron emission tomography and computed tomography before and after SABR were not predictive of clinical outcomes. Interestingly, PCEV levels and peripheral tumor-reactive CD8 T cells (TTR; CD8+ CD11ahigh) were correlated. CONCLUSIONS: This original study demonstrates that circulating PCEVs can serve as prognostic and predictive markers to SABR to identify patients with "true" omCRPC. In addition, it provides novel insights into the global crosstalk, mediated by PCEVs, between tumors and immune cells that leads to systemic suppression of immunity against CRPC. This work lays the foundation for future studies to investigate the underpinnings of metastatic progression and provide new therapeutic targets (eg, PCEVs) to improve SABR efficacy and clinical outcomes in treatment-resistant CRPC.

Calibration and standardization of extracellular vesicle measurements by flow cytometry for translational prostate cancer research.

Extracellular vesicles (EVs) are microscopic particles released naturally in biofluids by all cell types. Since EVs inherits genomic and proteomic patterns from the cell of origin, they are emerging as promising liquid biomarkers for human diseases. Flow cytometry is a popular method that is able to detect, characterize and determine the concentration of EVs with minimal sample preparation. However, the limited awareness of the scientific community to utilize standardization and calibration methods of flow cytometers is an important roadblock for data reproducibility and inter-laboratory comparison. A significant collaborative effort by the Extracellular Vesicle Flow Cytometry Working Group has led to the development of guidelines and best practices for using flow cytometry and reporting data in a way to improve rigor and reproducibility in EV research. At first look, standardization and calibration of flow cytometry for EV detection may seem burdensome and technically challenging for non-academic laboratories with limited technical training and knowledge in EV flow cytometry. In this study, we build on prior research efforts and provide a systematic approach to evaluate the performance of a high sensitivity flow cytometer (herein Apogee A60-Micro Plus) and fine-tune settings to improve detection sensitivity for EVs. We performed calibration of our flow cytometer to generate data with comparable units (nanometers, MESF). Finally, we applied our optimized protocol to measure the concentrations of prostate-derived EVs in healthy individuals and prostate cancer patients. In conclusion, our proof-of-feasibility study can serve as a scientific and technical framework for other groups motivated in using flow cytometry for EV research.

People with HIV receiving suppressive antiretroviral therapy show typical antibody durability after dual COVID-19 vaccination, and strong third dose responses.

Background: Longer-term humoral responses to two-dose COVID-19 vaccines remain incompletely characterized in people living with HIV (PLWH), as do initial responses to a third dose. Methods: We measured antibodies against the SARS-CoV-2 spike protein receptor-binding domain, ACE2 displacement and viral neutralization against wild-type and Omicron strains up to six months following two-dose vaccination, and one month following the third dose, in 99 PLWH receiving suppressive antiretroviral therapy, and 152 controls. Results: Though humoral responses naturally decline following two-dose vaccination, we found no evidence of lower antibody concentrations nor faster rates of antibody decline in PLWH compared to controls after accounting for sociodemographic, health and vaccine-related factors. We also found no evidence of poorer viral neutralization in PLWH after two doses, nor evidence that a low nadir CD4+ T-cell count compromised responses. Post-third-dose humoral responses substantially exceeded post-second-dose levels, though anti-Omicron responses were consistently weaker than against wild-type.Nevertheless, post-third-dose responses in PLWH were comparable to or higher than controls. An mRNA-1273 third dose was the strongest consistent correlate of higher post-third-dose responses. Conclusion: PLWH receiving suppressive antiretroviral therapy mount strong antibody responses after two- and three-dose COVID-19 vaccination. Results underscore the immune benefits of third doses in light of Omicron.

Humoral immune responses to COVID-19 vaccination in people living with HIV receiving suppressive antiretroviral therapy.

Humoral responses to COVID-19 vaccines in people living with HIV (PLWH) remain incompletely characterized. We measured circulating antibodies against the SARS-CoV-2 spike protein receptor-binding domain (RBD), ACE2 displacement and viral neutralization activities one month following the first and second COVID-19 vaccine doses, and again 3 months following the second dose, in 100 adult PLWH and 152 controls. All PLWH were receiving suppressive antiretroviral therapy, with median CD4+ T-cell counts of 710 (IQR 525-935) cells/mm3, though nadir CD4+ T-cell counts ranged as low as <10 cells/mm3. After adjustment for sociodemographic, health and vaccine-related variables, HIV infection was associated with lower anti-RBD antibody concentrations and ACE2 displacement activity after one vaccine dose. Following two doses however, HIV was not significantly associated with the magnitude of any humoral response after multivariable adjustment. Rather, older age, a higher burden of chronic health conditions, and dual ChAdOx1 vaccination were associated with lower responses after two vaccine doses. No significant correlation was observed between recent or nadir CD4+ T-cell counts and responses to two vaccine doses in PLWH. These results indicate that PLWH with well-controlled viral loads and CD4+ T-cell counts in a healthy range generally mount strong initial humoral responses to dual COVID-19 vaccination. Factors including age, co-morbidities, vaccine brand, response durability and the rise of new SARS-CoV-2 variants will influence when PLWH will benefit from additional doses. Further studies of PLWH who are not receiving antiretroviral treatment or who have low CD4+ T-cell counts are needed, as are longer-term assessments of response durability.

Heat recovery steam generator (HRSG) three-element drum level control utilizing Fractional order PID and fuzzy controllers.

Shrink and swell is a phenomenon that causes transient variability in water level once boiler load variation occurs. The leading cause of the swell effect is the steam demand changes and the actual arrangement of steam generating tubes in the boiler. Steam bubbles beneath HRSG drum water make the level control very difficult, particularly with significant disturbances in the input heat to HRSG. Plant shutdown may occur in some situations, and combined cycle plant efficiency is diminished. The recently applied control methods in industry are single-element and three-element control with PID controllers, but these methods are not well suited for substantial load changes. The main aim of this paper is to investigate the shrink and swell phenomenon inside HRSG power plants. In addition to the existing PID loops, two different standalone controllers, namely, the FOPID controller and fuzzy controller, are implemented with the HRSG model. Besides, Artificial Bee Colony (ABC) algorithm is used to tune FOPID efficiently. Based on overshoot, rise time, ISE, IAE, ITAE as performance measures, the comparison has been held between the three controllers. Simulations show that how the ABC optimization algorithm is efficient with PID, FOPID. It turns out that the proposed method is capable of improving system responses compared to the conventional optimal controller.

Accurate detection of Covid-19 patients based on Feature Correlated Naïve Bayes (FCNB) classification strategy.

The outbreak of Coronavirus (COVID-19) has spread between people around the world at a rapid rate so that the number of infected people and deaths is increasing quickly every day. Accordingly, it is a vital process to detect positive cases at an early stage for treatment and controlling the disease from spreading. Several medical tests had been applied for COVID-19 detection in certain injuries, but with limited efficiency. In this study, a new COVID-19 diagnosis strategy called Feature Correlated Naïve Bayes (FCNB) has been introduced. The FCNB consists of four phases, which are; Feature Selection Phase (FSP), Feature Clustering Phase (FCP), Master Feature Weighting Phase (MFWP), and Feature Correlated Naïve Bayes Phase (FCNBP). The FSP selects only the most effective features among the extracted features from laboratory tests for both COVID-19 patients and non-COVID-19 people by using the Genetic Algorithm as a wrapper method. The FCP constructs many clusters of features based on the selected features from FSP by using a novel clustering technique. These clusters of features are called Master Features (MFs) in which each MF contains a set of dependent features. The MFWP assigns a weight value to each MF by using a new weight calculation method. The FCNBP is used to classify patients based on the weighted Naïve Bayes algorithm with many modifications as the correlation between features. The proposed FCNB strategy has been compared to recent competitive techniques. Experimental results have proven the effectiveness of the FCNB strategy in which it outperforms recent competitive techniques because it achieves the maximum (99%) detection accuracy.

Brain Strategy Algorithm for Multiple Object Tracking Based on Merging Semantic Attributes and Appearance Features.

The human brain can effortlessly perform vision processes using the visual system, which helps solve multi-object tracking (MOT) problems. However, few algorithms simulate human strategies for solving MOT. Therefore, devising a method that simulates human activity in vision has become a good choice for improving MOT results, especially occlusion. Eight brain strategies have been studied from a cognitive perspective and imitated to build a novel algorithm. Two of these strategies gave our algorithm novel and outstanding results, rescuing saccades and stimulus attributes. First, rescue saccades were imitated by detecting the occlusion state in each frame, representing the critical situation that the human brain saccades toward. Then, stimulus attributes were mimicked by using semantic attributes to reidentify the person in these occlusion states. Our algorithm favourably performs on the MOT17 dataset compared to state-of-the-art trackers. In addition, we created a new dataset of 40,000 images, 190,000 annotations and 4 classes to train the detection model to detect occlusion and semantic attributes. The experimental results demonstrate that our new dataset achieves an outstanding performance on the scaled YOLOv4 detection model by achieving a 0.89 mAP 0.5.

Humoral immune responses to COVID-19 vaccination in people living with HIV receiving suppressive antiretroviral therapy.

Humoral responses to COVID-19 vaccines in people living with HIV (PLWH) remain incompletely understood. We measured circulating antibodies against the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein, ACE2 displacement and live viral neutralization activities one month following the first and second COVID-19 vaccine doses in 100 adult PLWH and 152 controls. All PLWH were receiving suppressive antiretroviral therapy, with median CD4+ T-cell counts of 710 (IQR 525-935) cells/mm 3 . Nadir CD4+ T-cell counts ranged as low as <10 (median 280; IQR 120-490) cells/mm 3 . After adjustment for sociodemographic, health and vaccine-related variables, HIV infection was significantly associated with 0.2 log 10 lower anti-RBD antibody concentrations (p=0.03) and ∻11% lower ACE2 displacement activity (p=0.02), but not lower viral neutralization (p=0.1) after one vaccine dose. Following two doses however, HIV was no longer significantly associated with the magnitude of any response measured. Rather, older age, a higher burden of chronic health conditions, and having received two ChAdOx1 doses (versus a heterologous or dual mRNA vaccine regimen) were independently associated with lower responses. After two vaccine doses, no significant correlation was observed between the most recent or nadir CD4+ T-cell counts and vaccine responses in PLWH. These results suggest that PLWH with well-controlled viral loads on antiretroviral therapy and CD4+ T-cell counts in a healthy range will generally not require a third COVID-19 vaccine dose as part of their initial immunization series, though other factors such as older age, co-morbidities, vaccine regimen type, and durability of vaccine responses will influence when this group may benefit from additional doses. Further studies of PLWH who are not receiving antiretroviral treatment and/or who have low CD4+ T-cell counts are needed.

Hybrid COVID-19 segmentation and recognition framework (HMB-HCF) using deep learning and genetic algorithms.

COVID-19 (Coronavirus) went through a rapid escalation until it became a pandemic disease. The normal and manual medical infection discovery may take few days and therefore computer science engineers can share in the development of the automatic diagnosis for fast detection of that disease. The study suggests a hybrid COVID-19 framework (named HMB-HCF) based on deep learning (DL), genetic algorithm (GA), weighted sum (WS), and majority voting principles in nine phases. Its segmentation phase suggests a lung segmentation algorithm using X-Ray images (named HMB-LSAXI) for extracting lungs. Its classification phase is built from a hybrid convolutional neural network (CNN) architecture using an abstractly-designed CNN (named HMB1-COVID19) and transfer learning (TL) pre-trained models (VGG16, VGG19, ResNet50, ResNet101, Xception, DenseNet121, DenseNet169, MobileNet, and MobileNetV2). The hybrid CNN architecture is used for learning, classification, and parameters optimization while GA is used to optimize the hyperparameters. This hybrid working mechanism is combined in an overall algorithm named HMB-DLGA. The study experiments implemented the WS approach to evaluate the models' performance using the loss, accuracy, F1-score, precision, recall, and area under curve (AUC) metrics with different pre-defined ratios. A collected, combined, and unified X-Ray dataset from 8 different public datasets was used alongside the regularization, dropout, and data augmentation techniques to limit the overall overfitting. The applied experiments reported state-of-the-art metrics. VGG16 reported 100% WS metric (i.e., 0.0097, 99.78%, 0.9984, 99.89%, 99.78%, and 0.9996 for the loss, accuracy, F1, precision, recall, and AUC respectively) concerning the highest WS. It also reported a 99.92% WS metric (i.e., 0.0099, 99.84%, 0.9984, 99.84%, 99.84%, and 0.9996 for the loss, accuracy, F1, precision, recall, and AUC respectively) concerning the last reported WS result. HMB-HCF was validated on 13 different public datasets to verify its generalization. The best-achieved metrics were compared with 13 related studies. These extensive experiments' target was the applicability verification and generalization.

Assessing Laparoscopic Surgical Skills Using Similarity Network Models: A Pilot Study.

Background: Medical devices are becoming more complex, and doctors need to learn quickly how to use new medical tools. However, it is challenging to objectively assess the fundamental laparoscopic surgical skill level and determine skill readiness for advancement. There is a lack of objective models to compare performance between medical trainees and experienced doctors. Methods: This article discusses the use of similarity network models for individual tasks and a combination of tasks to show the level of similarity between residents and medical students while performing each task and their overall laparoscopic surgical skill level using a medical device (eg laparoscopic instruments). When a medical student is connected to most residents, that student is competent to the next training level. Performance of sixteen participants (5 residents and 11 students) while performing 3 tasks in 3 different training schedules is used in this study. Results: The promising result shows the general positive progression of students over 4 training sessions. Our results also indicate that students with different training schedules have different performance levels. Students' progress in performing a task is quicker if the training sessions are held more closely compared to when the training sessions are far apart in time. Conclusions: This study provides a graph-based framework for evaluating new learners' performance on medical devices and their readiness for advancement. This similarity network method could be used to classify students' performance using similarity thresholds, facilitating decision-making related to training and progression through curricula.

A Multiomics Graph Database System for Biological Data Integration and Cancer Informatics.

The multiomics data are heterogeneous and come from different biological levels such as epigenetics, genomics, transcriptomics and proteomics. The development of high-throughput technologies has enabled researchers not only to study all the entities together but also to utilize information from different levels spanning DNA methylation, copy number variation (CNV), mutation, gene expression, and miRNA expression. With the recent advancement in image informatics, the field of radiomics is rapidly emerging. It can be expected that the information from microscopic images of the tissue will soon be part of many multiomics studies. Meanwhile, integration of different kinds of multiomics data to extract relevant biological information is currently a big challenge. This study is our ongoing effort to develop a model that properly integrates multiomics data and allows easy retrieval of information relevant to biological processes. In this article, we have enriched our previous graph database model to store gene expression, miRNA expression, DNA methylation, mutation, CNV, clinical data, including information of the image of tissue slides. To show that the model is working, we used data from the Cancer Genome Atlas for three cancer types.

Cutibacterium acnes Central Nervous System Catheter Infection Induces Long-Term Changes in the Cerebrospinal Fluid Proteome.

Cutibacterium acnes is the third most common cause of cerebrospinal fluid (CSF) shunt infection and is likely underdiagnosed due to the difficulty in culturing this pathogen. Shunt infections lead to grave neurologic morbidity for patients especially when there is a delay in diagnosis. Currently, the gold standard for identifying CSF shunt infections is microbiologic culture. However, C. acnes infection often results in falsely negative cultures; therefore, new diagnostic methods are needed. To investigate potential CSF biomarkers of C. acnes CSF shunt infection we adapted a rat model of CSF catheter infection to C. acnes. We found elevated levels of interleukin-1ß (IL-1ß), IL-6, chemokine ligand 2, and IL-10 in the CSF and brain tissues of animals implanted with C. acnes-infected catheters compared to sterile controls at day 1 postinfection. This coincided with modest increases in neutrophils in the CSF and, to a greater extent, in the brain tissues of animals with C. acnes infection, which closely mirrors the clinical findings in patients with C. acnes shunt infection. Mass spectrometry revealed that the CSF proteome is altered during C. acnes shunt infection and changes over the course of disease, typified at day 1 postinfection by an acute-phase and pathogen neutralization response evolving to a response consistent with wound resolution at day 28 compared to a sterile catheter placement. Collectively, these results demonstrate that it is possible to distinguish C. acnes infection from sterile postoperative inflammation and that CSF proteins could be useful in a diagnostic strategy for this pathogen that is difficult to diagnose.

Outcomes of modular femoral revision implants and the effect of component design on subsidence.

AIMS: Femoral revision component subsidence has been identified as predicting early failure in revision hip surgery. This comparative cohort study assessed the potential risk factors of subsidence in two commonly used femoral implant designs. METHODS: A comparative cohort study was undertaken, analyzing a consecutive series of patients following revision total hip arthroplasties using either a tapered-modular (TM) fluted titanium or a porous-coated cylindrical modular (PCM) titanium femoral component, between April 2006 and May 2018. Clinical and radiological assessment was compared for both treatment cohorts. Risk factors for subsidence were assessed and compared. RESULTS: In total, 65 TM and 35 PCM cases were included. At mean follow-up of seven years (1 to 13), subsidence was noted in both cohorts during the initial three months postoperatively (p < 0.001) then implants stabilized. Subsidence noted in 58.7% (38/65 cases) of the TM cohort (mean 2.3 mm, SD 3.5 mm) compared to 48.8% (17/35) of PCM cohort (mean 1.9 mm, SD 2.6 mm; p = 0.344). Subsidence of PCM cohort were significantly associated with extended trochanteric osteotomy (ETO) (p < 0.041). Although the ETO was used less frequently in PCM stem cohort (7/35), subsidence was noted in 85% (6/7) of them. Significant improvement of the final mean Oxford Hip Score (OHS) was reported in both treatment groups (p < 0.001). CONCLUSION: Both modular TM and PCM revision femoral components subsided within the femur. TM implants subsided more frequently than PCM components if the femur was intact but with no difference in clinical outcomes. However, if an ETO is performed then a PCM component will subside significantly more and suggests the use of a TM implant may be advisable. Cite this article: Bone Joint J 2020;102-B(6):709-715.

A novel graph theoretical approach for modeling microbiomes and inferring microbial ecological relationships.

BACKGROUND: Microbiomes play vital roles in shaping environments and stabilize them based on their compositions and inter-species relationships among its species. Variations in microbial properties have been reported to have significant impact on their host environment. For example, variants in gut microbiomes have been reported to be associated with several chronic conditions, such as inflammatory disease and irritable bowel syndrome. However, how microbial bacteria contribute to pathogenesis still remains unclear and major research questions in this domain remain unanswered. METHODS: We propose a split graph model to represent the composition and interactions of a given microbiome. We used metagenomes from Korean populations in this study. The dataset consists of three different types of samples, viz. mucosal tissue and stool from Crohn's disease patients and stool from healthy individuals. We use the split graph model to analyze the impact of microbial compositions on various host phenotypes. Utilizing the graph model, we have developed a pipeline that integrates genomic information and pathway analysis to characterize both critical informative components of inter-bacterial correlations and associations between bacterial taxa and various metabolic pathways. RESULTS: The obtained results highlight the importance of the microbial communities and their inter-relationships and show how these microbial structures are correlated with Crohn's disease. We show that there are significant positive associations between detected taxonomic biomarkers as well as multiple functional modules in the split graph of mucosal tissue samples from CD patients. Bacteria Moraxellaceae and Pseudomonadaceae were detected as taxonomic biomarkers in CD groups. Higher abundance of these bacteria have been reported in previous study and several metabolic pathways associated with these bacteria were characterized in CD samples. CONCLUSIONS: The proposed pipeline provides a new way to approach the analysis of complex microbiomes. The results obtained from this study show great potential in unraveling mechansims in complex biological systems to understand how various components in such complex environments are associated with critical biological functions.

Uncovering and characterizing splice variants associated with survival in lung cancer patients.

Splice variants have been shown to play an important role in tumor initiation and progression and can serve as novel cancer biomarkers. However, the clinical importance of individual splice variants and the mechanisms by which they can perturb cellular functions are still poorly understood. To address these issues, we developed an efficient and robust computational method to: (1) identify splice variants that are associated with patient survival in a statistically significant manner; and (2) predict rewired protein-protein interactions that may result from altered patterns of expression of such variants. We applied our method to the lung adenocarcinoma dataset from TCGA and identified splice variants that are significantly associated with patient survival and can alter protein-protein interactions. Among these variants, several are implicated in DNA repair through homologous recombination. To computationally validate our findings, we characterized the mutational signatures in patients, grouped by low and high expression of a splice variant associated with patient survival and involved in DNA repair. The results of the mutational signature analysis are in agreement with the molecular mechanism suggested by our method. To the best of our knowledge, this is the first attempt to build a computational approach to systematically identify splice variants associated with patient survival that can also generate experimentally testable, mechanistic hypotheses. Code for identifying survival-significant splice variants using the Null Empirically Estimated P-value method can be found at https://github.com/thecodingdoc/neep. Code for construction of Multi-Granularity Graphs to discover potential rewired protein interactions can be found at https://github.com/scwest/SINBAD.