Wearable sensors in patient acuity assessment in critical care.

Acuity assessments are vital for timely interventions and fair resource allocation in critical care settings. Conventional acuity scoring systems heavily depend on subjective patient assessments, leaving room for implicit bias and errors. These assessments are often manual, time-consuming, intermittent, and challenging to interpret accurately, especially for healthcare providers. This risk of bias and error is likely most pronounced in time-constrained and high-stakes environments, such as critical care settings. Furthermore, such scores do not incorporate other information, such as patients' mobility level, which can indicate recovery or deterioration in the intensive care unit (ICU), especially at a granular level. We hypothesized that wearable sensor data could assist in assessing patient acuity granularly, especially in conjunction with clinical data from electronic health records (EHR). In this prospective study, we evaluated the impact of integrating mobility data collected from wrist-worn accelerometers with clinical data obtained from EHR for estimating acuity. Accelerometry data were collected from 87 patients wearing accelerometers on their wrists in an academic hospital setting. The data was evaluated using five deep neural network models: VGG, ResNet, MobileNet, SqueezeNet, and a custom Transformer network. These models outperformed a rule-based clinical score (Sequential Organ Failure Assessment, SOFA) used as a baseline when predicting acuity state (for ground truth we labeled as unstable patients if they needed life-supporting therapies, and as stable otherwise), particularly regarding the precision, sensitivity, and F1 score. The results demonstrate that integrating accelerometer data with demographics and clinical variables improves predictive performance compared to traditional scoring systems in healthcare. Deep learning models consistently outperformed the SOFA score baseline across various scenarios, showing notable enhancements in metrics such as the area under the receiver operating characteristic (ROC) Curve (AUC), precision, sensitivity, specificity, and F1 score. The most comprehensive scenario, leveraging accelerometer, demographics, and clinical data, achieved the highest AUC of 0.73, compared to 0.53 when using SOFA score as the baseline, with significant improvements in precision (0.80 vs. 0.23), specificity (0.79 vs. 0.73), and F1 score (0.77 vs. 0.66). This study demonstrates a novel approach beyond the simplistic differentiation between stable and unstable conditions. By incorporating mobility and comprehensive patient information, we distinguish between these states in critically ill patients and capture essential nuances in physiology and functional status. Unlike rudimentary definitions, such as equating low blood pressure with instability, our methodology delves deeper, offering a more holistic understanding and potentially valuable insights for acuity assessment.

A Network Analysis of Digital Clock Drawing for Command and Copy Conditions.

Graphomotor and time-based variables from the digital Clock Drawing Test (dCDT) characterize cognitive functions. However, no prior publications have quantified the strength of the associations between digital clock variables as they are produced. We hypothesized that analysis of the production of clock features and their interrelationships, as suggested, will differ between the command and copy test conditions. Older adults aged 65+ completed a digital clock drawing to command and copy conditions. Using a Bayesian hill-climbing algorithm and bootstrapping (10,000 samples), we derived directed acyclic graphs (DAGs) to examine network structure for command and copy dCDT variables. Although the command condition showed moderate associations between variables (µ|ßz|= 0.34) relative to the copy condition (µ|ßz| = 0.25), the copy condition network had more connections (18/18 versus 15/18 command). Network connectivity across command and copy was most influenced by five of the 18 variables. The direction of dependencies followed the order of instructions better in the command condition network. Digitally acquired clock variables relate to one another but differ in network structure when derived from command or copy conditions. Continued analyses of clock drawing production should improve understanding of quintessential normal features to aid in early neurodegenerative disease detection.

C1QA and COMP: plasma-based biomarkers for early diagnosis of pancreatic neuroendocrine tumors.

Pancreatic Neuroendocrine tumors (PanNET) are challenging to diagnose and often detected at advanced stages due to a lack of specific and sensitive biomarkers. This study utilized proteomics as a valuable approach for cancer biomarker discovery; therefore, mass spectrometry-based proteomic profiling was conducted on plasma samples from 12 subjects (3 controls; 5 Grade I, 4 Grade II PanNET patients) to identify potential proteins capable of effectively distinguishing PanNET from healthy controls. Data are available via ProteomeXchange with the identifier PXD045045. 13.2% of proteins were uniquely identified in PanNET, while 60% were commonly expressed in PanNET and controls. 17 proteins exhibiting significant differential expression between PanNET and controls were identified with downstream analysis. Further, 5 proteins (C1QA, COMP, HSP90B1, ITGA2B, and FN1) were selected by pathway analysis and were validated using Western blot analysis. Significant downregulation of C1QA (p = 0.001: within groups, 0.03: control vs. grade I, 0.0013: grade I vs. grade II) and COMP (p = 0.011: within groups, 0.019: control vs grade I) were observed in PanNET Grade I & II than in controls. Subsequently, ELISA on 38 samples revealed significant downregulation of C1QA and COMP with increasing disease severity. This study shows the potential of C1QA and COMP in the early detection of PanNET, highlighting their role in the search for early-stage (Grade-I and Grade-II) diagnostic markers and therapeutic targets for PanNET.

FaIRClocks: Fair and Interpretable Representation of the Clock Drawing Test for mitigating classifier bias against lower educational groups.

The clock drawing test (CDT) is a neuropsychological assessment tool to evaluate a patient's cognitive ability. In this study, we developed a Fair and Interpretable Representation of Clock drawing tests (FaIRClocks) to evaluate and mitigate bias against people with lower education while predicting their cognitive status. We represented clock drawings with a 10-dimensional latent embedding using Relevance Factor Variational Autoencoder (RF-VAE) network pretrained on publicly available clock drawings from the National Health and Aging Trends Study (NHATS) dataset. These embeddings were later fine-tuned for predicting three cognitive scores: the Mini-Mental State Examination (MMSE) total score, attention composite z-score (ATT-C), and memory composite z-score (MEM-C). The classifiers were initially tested to see their relative performance in patients with low education (<= 8 years) versus patients with higher education (> 8 years). Results indicated that the initial unweighted classifiers confounded lower education with cognitive impairment, resulting in a 100% type I error rate for this group. Thereby, the samples were re-weighted using multiple fairness metrics to achieve balanced performance. In summary, we report the FaIRClocks model, which a) can identify attention and memory deficits using clock drawings and b) exhibits identical performance between people with higher and lower education levels.

Identification of potential biomarkers to predict organ morbidity in COVID-19: A repository based proteomics perspective.

SARS-CoV-2 causes substantial extrapulmonary manifestations in addition to pulmonary disease. Some of the major organs affected are cardiovascular, hematological and thrombotic, renal, neurological, and digestive systems. These types of muti-organ dysfunctions make it difficult and challenging for clinicians to manage and treat COVID-19 patients. The article focuses to identify potential protein biomarkers that can flag various organ systems affected in COVID-19. Publicly reposited high throughput proteomic data from human serum (HS), HEK293T/17 (HEK) and Vero E6 (VE) kidney cell culture were downloaded from ProteomeXchange consortium. The raw data was analyzed in Proteome Discoverer 2.4 to delineate the complete list of proteins in the three studies. These proteins were analyzed in Ingenuity Pathway Analysis (IPA) to associate them to various organ diseases. The shortlisted proteins were analyzed in MetaboAnalyst 5.0 to shortlist potential biomarker proteins. These were then assessed for disease-gene association in DisGeNET and validated by Protein-protein interactome (PPI) and functional enrichment studies (GO_BP, KEGG and Reactome pathways) in STRING. Protein profiling resulted in shortlisting 20 proteins in 7 organ systems. Of these 15 proteins showed at least 1.25-fold changes with a sensitivity and specificity of 70%. Association analysis further shortlisted 10 proteins with a potential association with 4 organ diseases. Validation studies established possible interacting networks and pathways affected, confirmingh the ability of 6 of these proteins to flag 4 different organ systems affected in COVID-19 disease. This study helps to establish a platform to seek protein signatures in different clinical phenotypes of COVID-19. The potential biomarker candidates that can flag organ systems involved are: (a) Vitamin K-dependent protein S and Antithrombin-III for hematological disorders; (b) Voltage-dependent anion-selective channel protein 1 for neurological disorders; (c) Filamin-A for cardiovascular disorder and, (d) Peptidyl-prolyl cis-trans isomerase A and Peptidyl-prolyl cis-trans isomerase FKBP1A for digestive disorders.

Explainable semi-supervised deep learning shows that dementia is associated with small, avocado-shaped clocks with irregularly placed hands.

The clock drawing test is a simple and inexpensive method to screen for cognitive frailties, including dementia. In this study, we used the relevance factor variational autoencoder (RF-VAE), a deep generative neural network, to represent digitized clock drawings from multiple institutions using an optimal number of disentangled latent factors. The model identified unique constructional features of clock drawings in a completely unsupervised manner. These factors were examined by domain experts to be novel and not extensively examined in prior research. The features were informative, as they distinguished dementia from non-dementia patients with an area under receiver operating characteristic (AUC) of 0.86 singly, and 0.96 when combined with participants' demographics. The correlation network of the features depicted the "typical dementia clock" as having a small size, a non-circular or "avocado-like" shape, and incorrectly placed hands. In summary, we report a RF-VAE network whose latent space encoded novel constructional features of clocks that classify dementia from non-dementia patients with high performance.

Label-Free Proteomics of Oral Mucosa Tissue to Identify Potential Biomarkers That Can Flag Predilection of Precancerous Lesions to Oral Cell Carcinoma: A Preliminary Study.

Oral squamous cell carcinomas are mostly preceded by precancerous lesions such as leukoplakia and erythroplakia. Our study is aimed at identifying potential biomarker proteins in precancerous lesions of leukoplakia and erythroplakia that can flag their transformation to oral cancer. Four biological replicate samples from clinical phenotypes of healthy control, leukoplakia, erythroplakia, and oral carcinoma were annotated based on clinical screening and histopathological evaluation of buccal mucosa tissue. Differentially expressed proteins were delineated using a label-free quantitative proteomic experiment done on an Orbitrap Fusion Tribrid mass spectrometer in three technical replicate sets of samples. Raw files were processed using MaxQuant version 2.0.1.0, and downstream analysis was done via Perseus version 1.6.15.0. Validation included functional annotation based on biological processes and pathways using the ClueGO plug-in of Cytoscape. Hierarchical clustering and principal component analysis were performed using the ClustVis tool. Across control, leukoplakia, and cancer, L-lactate dehydrogenase A chain, plectin, and WD repeat-containing protein 1 were upregulated, whereas thioredoxin 1 and spectrin alpha chain, nonerythrocytic 1 were downregulated. Across control, erythroplakia, and cancer, L-lactate dehydrogenase A chain was upregulated whereas aldehyde dehydrogenase 2, peroxiredoxin 1, heat shock 70 kDa protein 1B, and spectrin alpha chain, nonerythrocytic 1 were downregulated. We found that proteins involved in leukoplakia were associated with alteration in cytoskeletal disruption and glycolysis, while in erythroplakia, they were associated with alteration in response to oxidative stress and glycolysis across phenotypes. Hierarchical clustering subgrouped half of precancerous samples under the main branch of the control and the remaining half under carcinoma. Similarly, principal component analysis identified segregated clusters of control, precancerous lesions, and cancer, but erythroplakia phenotypes, in particular, overlapped more with the cancer cluster. Qualitative and quantitative protein signatures across control, precancer, and cancer phenotypes explain possible functional outcomes that dictate malignant transformation to oral carcinoma.

Diurnal Pain Classification in Critically Ill Patients using Machine Learning on Accelerometry and Analgesic Data.

Quantifying pain in patients admitted to intensive care units (ICUs) is challenging due to the increased prevalence of communication barriers in this patient population. Previous research has posited a positive correlation between pain and physical activity in critically ill patients. In this study, we advance this hypothesis by building machine learning classifiers to examine the ability of accelerometer data collected from daily wearables to predict self-reported pain levels experienced by patients in the ICU. We trained multiple Machine Learning (ML) models, including Logistic Regression, CatBoost, and XG-Boost, on statistical features extracted from the accelerometer data combined with previous pain measurements and patient demographics. Following previous studies that showed a change in pain sensitivity in ICU patients at night, we performed the task of pain classification separately for daytime and nighttime pain reports. In the pain versus no-pain classification setting, logistic regression gave the best classifier in daytime (AUC: 0.72, F1-score: 0.72), and CatBoost gave the best classifier at nighttime (AUC: 0.82, F1-score: 0.82). Performance of logistic regression dropped to 0.61 AUC, 0.62 F1-score (mild vs. moderate pain, nighttime), and CatBoost's performance was similarly affected with 0.61 AUC, 0.60 F1-score (moderate vs. severe pain, daytime). The inclusion of analgesic information benefited the classification between moderate and severe pain. SHAP analysis was conducted to find the most significant features in each setting. It assigned the highest importance to accelerometer-related features on all evaluated settings but also showed the contribution of the other features such as age and medications in specific contexts. In conclusion, accelerometer data combined with patient demographics and previous pain measurements can be used to screen painful from painless episodes in the ICU and can be combined with analgesic information to provide moderate classification between painful episodes of different severities.

Dynamic Delirium Prediction in the Intensive Care Unit using Machine Learning on Electronic Health Records.

Delirium is a syndrome of acute brain failure which is prevalent amongst older adults in the Intensive Care Unit (ICU). Incidence of delirium can significantly worsen prognosis and increase mortality, therefore necessitating its rapid and continual assessment in the ICU. Currently, the common approach for delirium assessment is manual and sporadic. Hence, there exists a critical need for a robust and automated system for predicting delirium in the ICU. In this work, we develop a machine learning (ML) system for real-time prediction of delirium using Electronic Health Record (EHR) data. Unlike prior approaches which provide one delirium prediction label per entire ICU stay, our approach provides predictions every 12 hours. We use the latest 12 hours of ICU data, along with patient demographic and medical history data, to predict delirium risk in the next 12-hour window. This enables delirium risk prediction as soon as 12 hours after ICU admission. We train and test four ML classification algorithms on longitudinal EHR data pertaining to 16,327 ICU stays of 13,395 patients covering a total of 56,297 12-hour windows in the ICU to predict the dynamic incidence of delirium. The best performing algorithm was Categorical Boosting which achieved an area under receiver operating characteristic curve (AUROC) of 0.87 (95% Confidence Interval; C.I, 0.86-0.87). The deployment of this ML system in ICUs can enable early identification of delirium, thereby reducing its deleterious impact on long-term adverse outcomes, such as ICU cost, length of stay and mortality.

EARLY DIFFERENTIATION BETWEEN SEPSIS AND STERILE INFLAMMATION VIA URINARY GENE SIGNATURES OF METABOLIC DYSREGULATION.

ABSTRACT: Objective: The aim of this study was to characterize early urinary gene expression differences between patients with sepsis and patients with sterile inflammation and summarize in terms of a reproducible sepsis probability score. Design: This was a prospective observational cohort study. Setting: The study was conducted in a quaternary care academic hospital. Patients: One hundred eighty-six sepsis patients and 78 systemic inflammatory response syndrome (SIRS) patients enrolled between January 2015 and February 2018. Interventions: Whole-genome transcriptomic analysis of RNA was extracted from urine obtained from sepsis patients within 12 hours of sepsis onset and from patients with surgery-acquired SIRS within 4 hours after major inpatient surgery. Measurements and Main Results: We identified 422 of 23,956 genes (1.7%) that were differentially expressed between sepsis and SIRS patients. Differentially expressed probes were provided to a collection of machine learning feature selection models to identify focused probe sets that differentiate between sepsis and SIRS. These probe sets were combined to find an optimal probe set (UrSepsisModel) and calculate a urinary sepsis score (UrSepsisScore), which is the geometric mean of downregulated genes subtracted from the geometric mean of upregulated genes. This approach summarizes the expression values of all decisive genes as a single sepsis score. The UrSepsisModel and UrSepsisScore achieved area under the receiver operating characteristic curves 0.91 (95% confidence interval, 0.86-0.96) and 0.80 (95% confidence interval, 0.70-0.88) on the validation cohort, respectively. Functional analyses of probes associated with sepsis demonstrated metabolic dysregulation manifest as reduced oxidative phosphorylation, decreased amino acid metabolism, and decreased oxidation of lipids and fatty acids. Conclusions: Whole-genome transcriptomic profiling of urinary cells revealed focused probe panels that can function as an early diagnostic tool for differentiating sepsis from sterile SIRS. Functional analysis of differentially expressed genes demonstrated a distinct metabolic dysregulation signature in sepsis.

2D-DIGE based urinary proteomics and functional enrichment studies to reveal novel Mycobacterium tuberculosis and human protein biomarker candidates for pulmonary tuberculosis.

In the absence of a sensitive and specific diagnostic modality capable of detecting all forms of tuberculosis (TB), proteomics may identify specific Mycobacterium tuberculosis (M.tb) proteins in urine, with a potential as biomarkers. To identify candidate biomarkers for TB, proteome profile of urine from pulmonary TB patients was compared with non-disease controls (NDC) and disease controls (DC, Streptococcus pneumonia infected patients) using a combination of two-dimensional difference gel electrophoresis (2D-DIGE) and liquid chromatography tandem mass spectrometry (LCMS/MS). Eleven differentially expressed host proteins and Eighteen high abundant M.tb proteins were identified. Protein-protein interactome (PPI) and functional enrichment analyses like Gene Ontologies, Reactome pathway etc. demonstrated that the human proteins mainly belong to extracellular space and show physiological pathways for immune response and hematological disorders. Whereas, M.tb proteins belong to the cell periphery, plasma membrane and cell wall, and demonstrated catalytic, nucleotide binding and ATPase activities along with other functional processes. The study findings provide valuable inputs about the biomarkers of TB and shed light on the probable disease consequences as an outcome of the bacterial pathogenicity.

iTRAQ proteomics of sentinel lymph nodes for identification of extracellular matrix proteins to flag metastasis in early breast cancer.

Patients with early breast cancer are affected by metastasis to axillary lymph nodes. Metastasis to these nodes is crucial for staging and quality of surgery. Sentinel Lymph Node Biopsy that is currently used to assess lymph node metastasis is not effective. This necessitates identification of biomarkers that can flag metastasis. Early stage breast cancer patients were recruited. Surgical resection of breast was followed by identification of sentinel lymph nodes. Fresh frozen section biopsy was used to assign metastatic and non-metastatic sentinel lymph nodes. Discovery phase included iTRAQ proteomics coupled with mass spectrometric analysis to identify differentially expressed proteins. Data is available via ProteomeXchange with identifier PXD027668. Validation was done by bioinformatic analysis and ELISA. There were 2398 unique protein groups and 109 differentially expressed proteins comparing metastatic and non-metastatic lymph nodes. Forty nine proteins were up-regulated, and sixty proteins that were down regulated in metastatic group. Bioinformatic analysis showed ECM-receptor interaction pathways to be implicated in lymph node metastasis. ELISA confirmed up-regulation of ECM proteins in metastatic lymph nodes. ECM proteins have requisite parameters to be developed as a diagnostic tool to assess status of sentinel lymph nodes to guide surgical intervention in early breast cancer.

Variational autoencoder provides proof of concept that compressing CDT to extremely low-dimensional space retains its ability of distinguishing dementia.

The clock drawing test (CDT) is an inexpensive tool to screen for dementia. In this study, we examined if a variational autoencoder (VAE) with only two latent variables can capture and encode clock drawing anomalies from a large dataset of unannotated CDTs (n = 13,580) using self-supervised pre-training and use them to classify dementia CDTs (n = 18) from non-dementia CDTs (n = 20). The model was independently validated using a larger cohort consisting of 41 dementia and 50 non-dementia clocks. The classification model built with the parsimonious VAE latent space adequately classified dementia from non-dementia (0.78 area under receiver operating characteristics (AUROC) in the original test dataset and 0.77 AUROC in the secondary validation dataset). The VAE-identified atypical clock features were then reviewed by domain experts and compared with existing literature on clock drawing errors. This study shows that a very small number of latent variables are sufficient to encode important clock drawing anomalies that are predictive of dementia.

Kinetic Isotope Effects on Electron Transfer Across Self-Assembled Monolayers on Gold.

Reactions requiring controlled delivery of protons and electrons are important in storage of energy in small molecules. While control over proton transfer can be achieved by installing appropriate chemical functionality in the catalyst, control of electron-transfer (ET) rates can be achieved by utilizing self-assembled monolayers (SAMs) on electrodes. Thus, a deeper understanding of the ET through SAM to an immobilized or covalently attached redox-active species is desirable. Long-range ET across several SAM-covered Au electrodes to covalently attached ferrocene is investigated using protonated and deuterated thiols (R-SH/R-SD). The rate of tunneling is measured using both chronoamperometry and cyclic voltammetry, and it shows a prominent kinetic isotope effect (KIE). The KIE is ∼2 (normal) for medium-chain-length thiols but ∼0.47 (inverse) for long-chain thiols. These results imply substantial contribution from the classical modes at the Au-(H)SR interface, which shifts substantially upon deuteration of the thiols, to the ET process. The underlying H/D KIE of these exchangeable thiol protons should be considered when analyzing solvent isotope effects in catalysis utilizing SAM.

Metabolomic Profiling for Diagnosis and Prognostication in Surgery: A Scoping Review.

OBJECTIVE: This review assimilates and critically evaluates available literature regarding the use of metabolomic profiling in surgical decision-making. BACKGROUND: Metabolomic profiling is performed by nuclear magnetic resonance spectroscopy or mass spectrometry of biofluids and tissues to quantify biomarkers (ie, sugars, amino acids, and lipids), producing diagnostic and prognostic information that has been applied among patients with cardiovascular disease, inflammatory bowel disease, cancer, and solid organ transplants. METHODS: PubMed was searched from 1995 to 2019 to identify studies investigating metabolomic profiling of surgical patients. Articles were included and assimilated into relevant categories per PRISMA-ScR guidelines. Results were summarized with descriptive analytical methods. RESULTS: Forty-seven studies were included, most of which were retrospective studies with small sample sizes using various combinations of analytic techniques and types of biofluids and tissues. Results suggest that metabolomic profiling has the potential to effectively screen for surgical diseases, suggest diagnoses, and predict outcomes such as postoperative complications and disease recurrence. Major barriers to clinical adoption include a lack of high-level evidence from prospective studies, heterogeneity in study design regarding tissue and biofluid procurement and analytical methods, and the absence of large, multicenter metabolome databases to facilitate systematic investigation of the efficacy, reproducibility, and generalizability of metabolomic profiling diagnoses and prognoses. CONCLUSIONS: Metabolomic profiling research would benefit from standardization of study design and analytic approaches. As technologies improve and knowledge garnered from research accumulates, metabolomic profiling has the potential to provide personalized diagnostic and prognostic information to support surgical decision-making from preoperative to postdischarge phases of care.

Discovery and Validation of Urinary Molecular Signature of Early Sepsis.

Identify alterations in gene expression unique to systemic and kidney-specific pathophysiologic processes using whole-genome analyses of RNA isolated from the urinary cells of sepsis patients. DESIGN: Prospective cohort study. SETTING: Quaternary care academic hospital. PATIENTS: A total of 266 sepsis and 82 control patients enrolled between January 2015 and February 2018. INTERVENTIONS: Whole-genome transcriptomic analysis of messenger RNA isolated from the urinary cells of sepsis patients within 12 hours of sepsis onset and from control subjects. MEASUREMENTS AND MAIN RESULTS: The differentially expressed probes that map to known genes were subjected to feature selection using multiple machine learning techniques to find the best subset of probes that differentiates sepsis from control subjects. Using differential expression augmented with machine learning ensembles, we identified a set of 239 genes in urine, which show excellent effectiveness in classifying septic patients from those with chronic systemic disease in both internal and independent external validation cohorts. Functional analysis indexes disrupted biological pathways in early sepsis and reveal key molecular networks driving its pathogenesis. CONCLUSIONS: We identified unique urinary gene expression profile in early sepsis. Future studies need to confirm whether this approach can complement blood transcriptomic approaches for sepsis diagnosis and prognostication.

The proteome and its dynamics: A missing piece for integrative multi-omics in schizophrenia.

The complex and heterogeneous pathophysiology of schizophrenia can be deconstructed by integration of large-scale datasets encompassing genes through behavioral phenotypes. Genome-wide datasets are now available for genetic, epigenetic and transcriptomic variations in schizophrenia, which are then analyzed by newly devised systems biology algorithms. A missing piece, however, is the inclusion of information on the proteome and its dynamics in schizophrenia. Proteomics has lagged behind omics of the genome, transcriptome and epigenome since analytic platforms were relatively less robust for proteins. There has been remarkable progress, however, in the instrumentation of liquid chromatography (LC) and mass spectrometry (MS) (LCMS), experimental paradigms and bioinformatics of the proteome. Here, we present a summary of methodological innovations of recent years in MS based proteomics and the power of new generation proteomics, review proteomics studies that have been conducted in schizophrenia to date, and propose how such data can be analyzed and integrated with other omics results. The function of a protein is determined by multiple molecular properties, i.e., subcellular localization, posttranslational modification (PTMs) and protein-protein interactions (PPIs). Incorporation of these properties poses additional challenges in proteomics and their integration with other omics; yet is a critical next step to close the loop of multi-omics integration. In sum, the recent advent of high-throughput proteome characterization technologies and novel mathematical approaches enable us to incorporate functional properties of the proteome to offer a comprehensive multi-omics based understanding of schizophrenia pathophysiology.

Efficacy, Safety and Steroid-sparing Effect of Topical Cyclosporine A 0.05% for Vernal Keratoconjunctivitis in Indian Children.

PURPOSE: To evaluate the efficacy, safety, and steroid-sparing effect of topical cyclosporine A (Cs A) 0.05% in patients with moderate to severe steroid dependent vernal keratoconjunctivitis (VKC). METHODS: A prospective, comparative, placebo controlled study was carried out on 68 VKC patients, with 34 patients treated with topical Cs A 0.05% and the remaining 34 with topical carboxymethyl cellulose 0.5% (placebo). Both groups also received topical loteprednol etabonate 0.5%. Symptom (itching, photophobia, tearing, and discharge) score, sign (tarsal and limbal papillae, corneal involvement, and conjunctival hyperemia) score, and drug score (steroid drop usage/day/eye) were recorded at baseline and each follow-up visit. The intraocular pressure (IOP) measurement and evaluation of any ocular side effects were carried out. RESULTS: Significant reduction in symptom score and sign score was seen in both groups. Cs A group significantly showed more reduction in symptom (P < 0.0001 in all follow-up visits) and sign (P < 0.0001 in all follow-up visits) scores compared to the placebo group. At day 7, mean steroid usage reduced from 4 to 3.44 ± 0.5 and 3.79 ± 0.4 in Cs A and placebo groups, respectively (P < 0.0001). Steroid drops completely stopped in 21 patients at day 60 in the Cs A group compared to none in the placebo group. No significant rise in IOP or any side effects were noted in either group. CONCLUSION: Topical Cs A 0.05% is effective and safe in patients with moderate to severe VKC with good steroid-sparing effect.

Visual Impairment Registry of Patients from North Kolkata, Eastern India: A Hospital-based Study.

PURPOSE: To study the demographic profile, severity and causes of visual impairment among registered patients in a tertiary care hospital in north Kolkata, eastern India, and to assess the magnitude of under-registration in that population. METHODS: This is a retrospective analytical study. A review of all visually impaired patients registered at our tertiary care hospital during a ten-year period from January 2005 to December 2014, which is entitled for certification of people of north Kolkata, eastern India (with a population denominator of 1.1 million), was performed. Overall, 2472 eyes of 1236 patients were analyzed in terms of demographic characteristics, cause of visual impairment, and percentage of visual disability as per the guidelines established by the government of India. RESULTS: Male patients (844, 68.28%; 95% confidence interval [CI], 65.69-70.87) registered more often than female patients (392; 31.72%, P = 0.0004). The registration rate for visual impairment was 11.24 per 100,000 per annum; this is not the true incidence rate, as both new patients and those visiting for renewal of certification were included in the study. Optic atrophy was the most common cause of visual impairment (384 eyes, 15.53%; 95% CI, 14.1-16.96). CONCLUSION: Commonest cause of visual impairment was optic atrophy followed by microphthalmos. Under-registration is a prevalent problem as the registration system is voluntary rather than mandatory, and female patients are more likely to be unregistered in this area.

Study of aqueous cytokines in patients with different patterns of diabetic macular edema based on optical coherence tomography.

PURPOSE: To compare aqueous angiogenic and inflammatory cytokine concentrations in different patterns of diabetic macular edema (DME) based on optical coherence tomography (OCT). METHODS: This prospective study was conducted between July 1, 2015, and March 31, 2016, for 9 months. Aqueous samples were obtained from 52 consecutive DME patients and 16 controls. DME patients were divided according to OCT patterns into diffuse retinal thickening (DRT; n = 17), cystoid macular edema (CME; n = 20) and serous retinal detachment (SRD; n = 15) groups. Interleukin (IL)-6, IL-8, vascular endothelial growth factor (VEGF) and tumor necrosis factor alpha (TNF-α) levels were measured by RayBio(R) Human ELISA Kit. RESULTS: IL-6, IL-8 and VEGF levels differed significantly between three DME groups (p < 0.001 in all cases), but the differences in TNF-α levels were not significant (p = 0.226). VEGF and IL-6 levels correlated with central foveal thickness in DRT and SRD groups, respectively. CONCLUSION: Aqueous cytokine levels vary with different morphological patterns of DME though the role of TNF-α needs to be studied further, and both anti-angiogenic and anti-inflammatory agents are required simultaneously for treatment of DME.