16S rRNA female reproductive microbiome investigation reveals Dalfopristin, Clorgyline, and Hydrazine as potential therapeutics for the treatment of bacterial vaginosis.

Bacterial vaginosis (BV) is a prevalent vaginal illness resulting from a disruption in the vaginal microbial equilibrium. The vaginal microbiota has been shown to have a substantial impact on the development and continuation of BV. This work utilized 16S rRNA sequence analysis of vaginal microbiome samples (Control vs BV samples) utilizing Parallel-Meta 3 to investigate the variations in microbial composition. The unique genes identified were used to determine prospective therapeutic targets and their corresponding inhibitory ligands. Further, molecular docking was conducted and then MD simulations were carried out to confirm the docking outcomes. In the BV samples, we detected several anaerobic bacteria recognized for their ability to generate biofilms, namely Acetohalobium, Anaerolineaceae, Desulfobacteraceae, and others. Furthermore, we identified Dalfopristin, Clorgyline, and Hydrazine as potential therapeutic options for the management of BV. This research provides new insights into the causes of BV and shows the potential effectiveness of novel pharmacological treatments.

Deep Inception-ResNet: A Novel Approach for Personalized Prediction of Cumulative Pregnancy Outcomes in Vitro Fertilization Treatment (IVF).

Background: Infertility is one of the major causes of socioeconomic stress worldwide due to social stigma and stressful lifestyles. Despite technological advances, couples still undergo several IVF cycles for conceiving without knowing their true prognosis which is causing a huge social and medical impact, and the live birth rate continues to be relatively low (~ 25%). A prediction model that predicts IVF prognosis accurately considering the pre-treatment parameters before starting the IVF cycle will help clinicians and patients to make better-informed choices. Methods: In this study, clinical details of 2268 patients with 79 features who underwent IVF/ICSI procedure from January 2018 to December 2020, at the Center of IVF and Human Reproduction, Sir Ganga Ram Hospital were retrospectively collected. The machine learning model was developed considering features such as maternal age, number of IVF cycle, type of infertility, duration of infertility, AMH, indication for IVF, sperm type, BMI, embryo transfer, and ß-hCG value at the end of a fresh cycle and/or one subsequent frozen embryo transfer cycle was selected as the measure of outcome. Results: Compared to other classifiers, for an 80:20 train-test split with feature selection, the proposed Deep Inception-Residual Network architecture-based neural network gave the best accuracy (76%) and ROC-AUC score of 0.80. For tabular datasets, the applied approach has remained unexplored in previously made studies for reproductive health. Conclusion: This model is the starting point for providing a personalized prediction of a successful outcome for an infertile couple before they enter the IVF procedure. Supplementary Information: The online version contains supplementary material available at 10.1007/s13224-023-01773-9.

Systems-wide analysis of A. fumigatus using kinetic modeling of metabolic pathways to identify putative drug targets.

Aspergillosis is a major causative factor for morbidity in those with impaired immune systems, often caused by Aspergillus fumigatus. The diagnosis and treatment are difficult due to the diversity of individuals and risk factors and still pose a challenge for medical professionals. To understand the pathogenicity of any organism, it is critical to identify the significant metabolic pathways that are involved. Our work focused on developing kinetic models of critical pathways crucial for the survival of A. fumigatus using COPASI. While focusing on the folate biosynthesis, ergosterol biosynthesis and glycolytic pathway; sensitivity, time-course and steady-state analysis were performed to find the proteins/enzymes that are essential in the pathway and can be considered as potential drug targets. For further analysis of the interaction of drug targets identified, a protein-protein interaction (PPI) network was built, and hub nodes were identified using the Cytohubba package from Cytoscape. Based on the findings, dihydropteroate-synthase, dihydrofolate-reductase, 4-amino-4-deoxychorismate synthase, HMG-CoA-reductase, PG-isomerase and hexokinase could act as potential drug targets. Further, molecular docking and MM-GBSA analysis were performed with ligands chosen from DrugBank, and PubChem, and validated by experimental evidence and existing literature based on results from kinetic modeling and PPI network analysis. Based on docking scores and MM-GBSA results, molecular simulations were carried out for 1AJ2-dapsone, 1DIS-sulfamethazine, 1T02-lovastatin and 70YL-3-bromopyruvic acid complexes, which validated our findings. Our study provides a deeper insight into the mechanisms of A. fumigatus's metabolism to reveal dapsone, sulfamethazine, lovastatin and 3-bromopyruvic acid as potential drugs for the treatment of Aspergillosis.Communicated by Ramaswamy H. Sarma.

Identification of QTNs Associated With Flowering Time, Maturity, and Plant Height Traits in Linum usitatissimum L. Using Genome-Wide Association Study.

Early flowering, maturity, and plant height are important traits for linseed to fit in rice fallows, for rainfed agriculture, and for economically viable cultivation. Here, Multi-Locus Genome-Wide Association Study (ML-GWAS) was undertaken in an association mapping panel of 131 accessions, genotyped using 68,925 SNPs identified by genotyping by sequencing approach. Phenotypic evaluation data of five environments comprising 3 years and two locations were used. GWAS was performed for three flowering time traits including days to 5%, 50%, and 95% flowering, days to maturity, and plant height by employing five ML-GWAS methods: FASTmrEMMA, FASTmrMLM, ISIS EM-BLASSO, mrMLM, and pLARmEB. A total of 335 unique QTNs have been identified for five traits across five environments. 109 QTNs were stable as observed in ≥2 methods and/or environments, explaining up to 36.6% phenotypic variance. For three flowering time traits, days to maturity, and plant height, 53, 30, and 27 stable QTNs, respectively, were identified. Candidate genes having roles in flower, pollen, embryo, seed and fruit development, and xylem/phloem histogenesis have been identified. Gene expression of candidate genes for flowering and plant height were studied using transcriptome of an early maturing variety Sharda (IC0523807). The present study unravels QTNs/candidate genes underlying complex flowering, days to maturity, and plant height traits in linseed.

PluriMetNet: A dynamic electronic model decrypting the metabolic variations in human embryonic stem cells (hESCs) at fluctuating oxygen concentrations.

Stem cells are an excellent resource in translational medicine however much is known only in terms of transcriptional and epigenetic regulation of human embryonic stem cells (hESCs). Metabolic regulation of hESCs is still unexplored in many ways, particularly the role of energy metabolism, which is intrinsic to the maintenance of cell viability, however, is very little explored in the past years. Also, there exists no hESC specific core metabolic model of pluripotency as per our knowledge. Through our work, we establish such a metabolic model of hESC using combinatorial in-silico approach of genome scale model reduction and literature curation. Further, through perturbations taking oxygen as a parameter we propose that under lower levels of oxygen concentration there is a significant dynamic change in the energy metabolism of the hESC. We further investigated energy subsystem pathways and their respective reactions in order to locate the direction of energy production along with the dynamic of nutrient metabolites like glucose and glutamine. The output shows a steep increment/decrement at a certain oxygen range. These sharp increments/decrements under hypoxic conditions are termed here as a critical range for hESC metabolic pathway. The data also resonates with the previous experimental studies on hESC energy metabolism confirming the robustness of our model. The model helps to extract range for different pathways in the energy subsystem, making us a little closer in understanding the metabolism of hESC. We also demonstrated the possible range of pathway changes in hESC's energy metabolism that can serve as the crucial preliminary data for further prospective studies. The model also offers a promise in the prediction of the flux behaviour of various metabolites in hESC.Communicated by Ramaswamy H. Sarma.

Gene expression, molecular docking, and molecular dynamics studies to identify potential antifungal compounds targeting virulence proteins/genes VelB and THR as possible drug targets against Curvularia lunata.

Curvuluria lunata is a melanized fungus pathogenic to both plants and animals including humans, causing from mild, febrile to life-threatening illness if not well treated. In humans, it is an etiological agent of keratomycosis, sinusitis, and onychomycosis in immunocompromised and immunocompetent patients. The development of multiple-drug-resistant strains poses a critical treatment issue as well as public health problem. Natural products are attractive prototypes for drug discovery due to their broad-spectrum efficacy and lower side effects. The present study explores possible targets of natural antifungal compounds (α-pinene, eugenol, berberine, and curcumin) against C. lunata via gene expression analysis, molecular docking interaction, and molecular dynamics (MD) studies. Curcumin, berberine, eugenol, and α-pinene exhibited in vitro antifungal activity at 78 µg/ml, 156 µg/ml, 156 µg/ml, and 1250 µg/ml, respectively. In addition, treatment by these compounds led to the complete inhibition of conidial germination and hindered the adherence when observed on onion epidermis. Several pathogenic factors of fungi are crucial for their survival inside the host including those involved in melanin biosynthesis, hyphal growth, sporulation, and mitogen-activated protein kinase (MAPK) signalling. Relative gene expression of velB, brn1, clm1, and pks18 responsible for conidiation, melanin, and cell wall integrity was down-regulated significantly. Results of molecular docking possessed good binding affinity of compounds and have confirmed their potential targets as THR and VelB proteins. The docked structures, having good binding affinity among all, were further refined, and rescored from their docked poses through 100-ns long MD simulations. The MDS study revealed that curcumin formed a stable and energetically stabilized complex with the target protein. Therefore, the study concludes that the antifungal compounds possess significant efficacy to inhibit C. lunata growth targeting virulence proteins/genes involved in spore formation and melanin biosynthesis.

Proteome analysis using machine learning approaches and its applications to diseases.

With the tremendous developments in the fields of biological and medical technologies, huge amounts of data are generated in the form of genomic data, images in medical databases or as data on protein sequences, and so on. Analyzing this data through different tools sheds light on the particulars of the disease and our body's reactions to it, thus, aiding our understanding of the human health. Most useful of these tools is artificial intelligence and deep learning (DL). The artificially created neural networks in DL algorithms help extract viable data from the datasets, and further, to recognize patters in these complex datasets. Therefore, as a part of machine learning, DL helps us face all the various challenges that come forth during protein prediction, protein identification and their quantification. Proteomics is the study of such proteins, their structures, features, properties and so on. As a form of data science, Proteomics has helped us progress excellently in the field of genomics technologies. One of the major techniques used in proteomics studies is mass spectrometry (MS). However, MS is efficient with analysis of large datasets only with the added help of informatics approaches for data analysis and interpretation; these mainly include machine learning and deep learning algorithms. In this chapter, we will discuss in detail the applications of deep learning and various algorithms of machine learning in proteomics.

Towards optogenetic vision restoration with high resolution.

In many cases of inherited retinal degenerations, ganglion cells are spared despite photoreceptor cell death, making it possible to stimulate them to restore visual function. Several studies have shown that it is possible to express an optogenetic protein in ganglion cells and make them light sensitive, a promising strategy to restore vision. However the spatial resolution of optogenetically-reactivated retinas has rarely been measured, especially in the primate. Since the optogenetic protein is also expressed in axons, it is unclear if these neurons will only be sensitive to the stimulation of a small region covering their somas and dendrites, or if they will also respond to any stimulation overlapping with their axon, dramatically impairing spatial resolution. Here we recorded responses of mouse and macaque retinas to random checkerboard patterns following an in vivo optogenetic therapy. We show that optogenetically activated ganglion cells are each sensitive to a small region of visual space. A simple model based on this small receptive field predicted accurately their responses to complex stimuli. From this model, we simulated how the entire population of light sensitive ganglion cells would respond to letters of different sizes. We then estimated the maximal acuity expected by a patient, assuming it could make an optimal use of the information delivered by this reactivated retina. The obtained acuity is above the limit of legal blindness. Our model also makes interesting predictions on how acuity might vary upon changing the therapeutic strategy, assuming an optimal use of the information present in the retinal activity. Optogenetic therapy could thus potentially lead to high resolution vision, under conditions that our model helps to determinine.

VIRdb: a comprehensive database for interactive analysis of genes/proteins involved in the pathogenesis of vitiligo.

Vitiligo is a chronic asymptomatic disorder affecting melanocytes from the basal layer of the epidermis which leads to a patchy loss of skin color. Even though it is one of the neglected disease conditions, people suffering from vitiligo are more prone to psychological disorders. As of now, various studies have been done in order to project auto-immune implications as the root cause. To understand the complexity of vitiligo, we propose the Vitiligo Information Resource (VIRdb) that integrates both the drug-target and systems approach to produce a comprehensive repository entirely devoted to vitiligo, along with curated information at both protein level and gene level along with potential therapeutics leads. These 25,041 natural compounds are curated from Natural Product Activity and Species Source Database. VIRdb is an attempt to accelerate the drug discovery process and laboratory trials for vitiligo through the computationally derived potential drugs. It is an exhaustive resource consisting of 129 differentially expressed genes, which are validated through gene ontology and pathway enrichment analysis. We also report 22 genes through enrichment analysis which are involved in the regulation of epithelial cell differentiation. At the protein level, 40 curated protein target molecules along with their natural hits that are derived through virtual screening. We also demonstrate the utility of the VIRdb by exploring the Protein-Protein Interaction Network and Gene-Gene Interaction Network of the target proteins and differentially expressed genes. For maintaining the quality and standard of the data in the VIRdb, the gold standard in bioinformatics toolkits like Cytoscape, Schrödinger's GLIDE, along with the server installation of MATLAB, are used for generating results. VIRdb can be accessed through "http://www.vitiligoinfores.com/".

VIRdb 2.0: Interactive analysis of comorbidity conditions associated with vitiligo pathogenesis using co-expression network-based approach.

Vitiligo is a disease of mysterious origins in the context of its occurrence and pathogenesis. The autoinflammatory theory is perhaps the most widely accepted theory that discusses the occurrence of Vitiligo. The theory elaborates the clinical association of vitiligo with autoimmune disorders such as Psoriasis, Multiple Sclerosis and Rheumatoid Arthritis and Diabetes. In the present work, we discuss the comprehensive set of differentially co-expressed genes involved in the crosstalk events between Vitiligo and associated autoimmune disorders (Psoriasis, Multiple Sclerosis and Rheumatoid Arthritis). We progress our previous tool, Vitiligo Information Resource (VIRdb), and incorporate into it a compendium of Vitiligo-related multi-omics datasets and present it as VIRdb 2.0. It is available as a web-resource consisting of statistically sound and manually curated information. VIRdb 2.0 is an integrative database as its datasets are connected to KEGG, STRING, GeneCards, SwissProt, NPASS. Through the present study, we communicate the major updates and expansions in the VIRdb and deliver the new version as VIRdb 2.0. VIRdb 2.0 offers the maximum user interactivity along with ease of navigation. We envision that VIRdb 2.0 will be pertinent for the researchers and clinicians engaged in drug development for vitiligo.

Sphingosine-1-phosphate receptor 3 in the medial prefrontal cortex promotes stress resilience by reducing inflammatory processes.

Stress can promote the development of psychiatric disorders, though some individuals are more vulnerable to stress compared to others who are more resilient. Here we show that the sphingosine-1-phosphate receptor 3 (S1PR3) in the medial prefrontal cortex (mPFC) of rats regulates resilience to chronic social defeat stress. S1PR3 expression is elevated in the mPFC of resilient compared to vulnerable and control rats. Virally-mediated over-expression of S1PR3 in the mPFC produces a resilient phenotype whereas its knock-down produces a vulnerable phenotype, characterized by increased anxiety- and depressive-like behaviors, and these effects are mediated by TNFα. Furthermore, we show that S1PR3 mRNA in blood is reduced in veterans with PTSD compared to combat-exposed control subjects and its expression negatively correlates with symptom severity. Together, these data identify S1PR3 as a regulator of stress resilience and reveal sphingolipid receptors as important substrates of relevance to stress-related psychiatric disorders.

Construction of Discrete Model of Human Pluripotency in Predicting Lineage-Specific Outcomes and Targeted Knockdowns of Essential Genes.

A network consisting of 45 core genes was developed for the genes/proteins responsible for loss/gain of function in human pluripotent stem cells. The nodes were included on the basis of literature curation. The initial network topology was further refined by constructing an inferred Boolean model from time-series RNA-seq expression data. The final Boolean network was obtained by integration of the initial topology and the inferred topology into a refined model termed as the integrated model. Expression levels were observed to be bi-modular for most of the genes involved in the mechanism of human pluripotency. Thus, single and combinatorial perturbations/knockdowns were executed using an in silico approach. The model perturbations were validated with literature studies. A number of outcomes are predicted using the knockdowns of the core pluripotency circuit and we are able to establish the minimum requirement for maintenance of pluripotency in human. The network model is able to predict lineage-specific outcomes and targeted knockdowns of essential genes involved in human pluripotency which are challenging to perform due to ethical constraints surrounding human embryonic stem cells.

Cleft-Related Infanticide and Abandonment: A Systematic Review of the Academic and Lay Literature.

OBJECTIVE: We aimed to describe the scope of cleft-related infanticide and identify issues that might inform prevention strategies. DESIGN: Systematic reviews of both academic (eg, PubMed, EBSCOhost) and lay literature (eg, LexisNexis Academic, Google) databases were performed to identify all primary reports of cleft-related infanticide. All languages were included. Records before 1985 were excluded. Reference lists of all included reports were screened for potentially relevant records. MAIN OUTCOME MEASURES: Country of origin and excerpts that pertained to the concepts surrounding cleft-related infanticide were extracted. Extracted excerpts were examined using a content analysis framework. RESULTS: Of the 1,151 records retrieved, 70 reports documented cleft-related infanticide from 27 countries. The largest number of reports was from China (14 reports; 48% of reports), followed by India (4; 14%) and Nigeria (4; 14%). However, 2 countries had 3 reports, 5 countries had 2 reports, and 17 countries had 1 report. Themes that emerged from excerpt analysis included stigma, lack of affordable cleft care, abandonment, orphanage overcrowding, and abuse and slavery. CONCLUSIONS: Cleft-related infanticide is a global problem. Initiatives to sensitize communities to cleft lip and/or cleft palate, provide timely and affordable cleft care, and build support systems for affected families may prove beneficial. Cleft care organizations have the opportunity to advocate for these initiatives, reduce the incidence of infanticide by providing or supporting timely and affordable cleft care, and demonstrate that children with successful cleft repairs reassimilate well into their communities.

Molecular Epidemiology of Extended-Spectrum ß-Lactamase-Producing Escherichia coli Pathotypes in Diarrheal Children from Low Socioeconomic Status Communities in Bihar, India: Emergence of the CTX-M Type.

BACKGROUND: Childhood diarrheal diseases remain highly endemic in India, but the emergence of extended-spectrum ß-lactamase (ESBL)-producing Escherichia coli among children with diarrhea in Bihar remains elusive. In this study, we determine and characterize ESBL-producing E coli pathotypes among hospitalized diarrheal preschool children living in low socioeconomic level communities in Bihar, India. MATERIALS AND METHODS: The stool samples were collected everyday throughout the year for 2 consecutive years. In our study, we collected stool samples randomly from every fifth patient. Stool samples were collected from a total of 633 randomly selected diarrheal children (age: 0-60 months) belonging to 17 communities and screened for identification of virulent diarrheagenic E coli (DEC) pathotype (viz, enteropathogenic E coli [EPEC], enteroaggregative E coli [EAEC], enterotoxigenic E coli [ETEC], enteroinvasive E coli [EIEC], and enterohemorrhagic E coli [EHEC]) by a multiplex polymerase chain reaction (PCR) assay. Furthermore, ESBLs were screened by conventional antibiotic resistance pattern testing and later characterized for the presence of ß-lactamase (bla) genes by PCR and DNA sequencing. RESULTS: Diarrheagenic E coli was detected in 191 cases (30.2%) of the total 633 diarrheic children. Maximum occurrence of DEC was found in ≤12 months age group (72.7%) with prevalence of the EAEC pathotype. Most isolates were resistant to ampicillin, ciprofloxacin, piperacillin, levofloxacin, ceftazidime, cefotaxime, ceftriaxone, and gentamicin, whereas over 96% of them were sensitive to amikacin. About 37.6% of total 191 DEC isolates were ESBL producers (n = 72), being prevalent among ETEC (n = 35; 18.32%), followed by EPEC (n = 21; 10.9%), EAEC (n = 13; 6.8%), and EIEC (n = 3; 1.57%). Interestingly, the commonest ß-lactamase was CTX-M type (blaCTX-M) in 86.1% (n = 62) of the ESBL isolates, followed by blaSHV (n = 49; 68%), blaTEM (n = 37; 51.8%), and blaOXA (n = 21; 29.1%) determinants. Resistance of ESBL isolates was mostly related to ampicillin (100%), ceftriaxone (98.1%), cefotaxime (92.4%), gentamicin (74.1%), and levofloxacin (73.2%), whereas best antimicrobial activities were observed for piperacillin-tazobactam, amikacin, meropenem, and imipenem. CONCLUSIONS: This study revealed that EAEC (72.1%) is the predominant pathotype in Bihar, significantly high in ≤12 months age group children (P = .04). Moreover, the widespread prevalence of ESBLs in children, especially the CTX-M type, is of great concern, which requires monitoring of infection control measures through efficient antimicrobial management and detection of ESBL-producing isolates.

Prediction and imputation in irregularly sampled clinical time series data using hierarchical linear dynamical models.

Clinical time series, comprising of repeated clinical measurements provide valuable information of the trajectory of patients' condition. Linear dynamical systems (LDS) are used extensively in science and engineering for modeling time series data. The observation and state variables in LDS are assumed to be uniformly sampled in time with a fixed sampling rate. The observation sequence for clinical time series is often irregularly sampled and LDS do not model such data well. In this paper, we develop two LDS-based models for irregularly sampled data. The key idea is to incorporate a temporal difference variable within the state equations of LDS whose parameters are estimated using observed data. Our models are evaluated on prediction and imputation tasks using real irregularly sampled clinical time series data and are found to outperform state-of-the-art techniques.

Transcription Factor Information System (TFIS): A Tool for Detection of Transcription Factor Binding Sites.

Transcription factors are trans-acting proteins that interact with specific nucleotide sequences known as transcription factor binding site (TFBS), and these interactions are implicated in regulation of the gene expression. Regulation of transcriptional activation of a gene often involves multiple interactions of transcription factors with various sequence elements. Identification of these sequence elements is the first step in understanding the underlying molecular mechanism(s) that regulate the gene expression. For in silico identification of these sequence elements, we have developed an online computational tool named transcription factor information system (TFIS) for detecting TFBS for the first time using a collection of JAVA programs and is mainly based on TFBS detection using position weight matrix (PWM). The database used for obtaining position frequency matrices (PFM) is JASPAR and HOCOMOCO, which is an open-access database of transcription factor binding profiles. Pseudo-counts are used while converting PFM to PWM, and TFBS detection is carried out on the basis of percent score taken as threshold value. TFIS is equipped with advanced features such as direct sequence retrieving from NCBI database using gene identification number and accession number, detecting binding site for common TF in a batch of gene sequences, and TFBS detection after generating PWM from known raw binding sequences in addition to general detection methods. TFIS can detect the presence of potential TFBSs in both the directions at the same time. This feature increases its efficiency. And the results for this dual detection are presented in different colors specific to the orientation of the binding site. Results obtained by the TFIS are more detailed and specific to the detected TFs as integration of more informative links from various related web servers are added in the result pages like Gene Ontology, PAZAR database and Transcription Factor Encyclopedia in addition to NCBI and UniProt. Common TFs like SP1, AP1 and NF-KB of the Amyloid beta precursor gene is easily detected using TFIS along with multiple binding sites. In another scenario of embryonic developmental process, TFs of the FOX family (FOXL1 and FOXC1) were also identified. TFIS is platform-independent which is publicly available along with its support and documentation at http://tfistool.appspot.com and http://www.bioinfoplus.com/tfis/ . TFIS is licensed under the GNU General Public License, version 3 (GPL-3.0).

Red-shifted channelrhodopsin stimulation restores light responses in blind mice, macaque retina, and human retina.

Targeting the photosensitive ion channel channelrhodopsin-2 (ChR2) to the retinal circuitry downstream of photoreceptors holds promise in treating vision loss caused by retinal degeneration. However, the high intensity of blue light necessary to activate channelrhodopsin-2 exceeds the safety threshold of retinal illumination because of its strong potential to induce photochemical damage. In contrast, the damage potential of red-shifted light is vastly lower than that of blue light. Here, we show that a red-shifted channelrhodopsin (ReaChR), delivered by AAV injections in blind rd1 mice, enables restoration of light responses at the retinal, cortical, and behavioral levels, using orange light at intensities below the safety threshold for the human retina. We further show that postmortem macaque retinae infected with AAV-ReaChR can respond with spike trains to orange light at safe intensities. Finally, to directly address the question of translatability to human subjects, we demonstrate for the first time, AAV- and lentivirus-mediated optogenetic spike responses in ganglion cells of the postmortem human retina.

Appearance of the Retina With Full-Field Optical Coherence Tomography.

PURPOSE: To interpret full-field optical coherence tomography (FFOCT) images of ex vivo retina. METHODS: Flatmounted retinas of human, primate, pig, sheep, rat, and mouse were imaged using FFOCT. To identify retinal ganglion and amacrine cells, fixed samples immunolabeled against Tuj1 and Brn3a or live samples labeled in vitro with green fluorescent protein (GFP) were analyzed by combining FFOCT, fluorescence confocal microscopy (FCM), and fluorescence-FFOCT. To investigate postmortem tissue changes, time series were acquired over 48 hours and on fresh versus fixed tissue. RESULTS: With FFOCT, cell types and features such as nerve fiber bundles and RGC somas were resolved without use of contrast agents at 1-µm xyz resolution. Cell somas in the ganglion cell layer (GCL) in large mammals appeared predominantly bright with dark contours, while in rodents, GCL somas appeared dark with bright contours. RGC axon to soma junctions could be traced in the three-dimensional (3D) image stacks. Time series revealed undulation of retinal tissue samples over 48 hours, though no degradation of individual cells was detected, while paraformaldehyde fixation caused increased scattering and shrinkage. CONCLUSIONS: Full-field OCT reveals micrometric morphologic detail in the retina without the use of contrast agents. We observed interspecies differences in optical properties of GCL somas. Fixation significantly alters retinal transparency hence reducing the visibility of microscopic features.

Estimate of Unmet Need for Cleft Lip and/or Palate Surgery in India.

IMPORTANCE: The unmet need for cleft lip and/or palate (CL/P) care in India is significant. However, estimates required for CL/P care program planning are lacking. OBJECTIVE: To estimate the unmet need for CL/P surgery in India at the state level. DESIGN, SETTING, AND PARTICIPANTS: To determine the proportion of individuals with CL/P who presented for care in India, data were used from patients who received care at Operation Smile programs in 12 low- and middle-income countries from June 1, 2013, to May 31, 2014. The resulting model describes the prevalent unmet need for cleft surgery in India by state and includes patients older than the surgery target ages of 1 and 2 years for cleft lip and cleft palate repair, respectively. Next, the total number of unrepaired CL/P cases in each state was estimated using state-level economic and health system indicators. MAIN OUTCOMES AND MEASURES: Prevalent unmet need for CL/P repair. RESULTS: In the 28 states with available data, an estimated 72 637 cases of unrepaired CL/P (uncertainty interval, 58 644-97 870 cases) were detected. The percentage of individuals with unrepaired CL/P who were older than the respective target ages ranged from 37.0% (95% CI, 30.6%-43.8%) in Goa to 65.8% (95% CI, 60.3%-70.9%) in Bihar (median, 57.9%; interquartile range, 52.6%-63.4%). The rate of unrepaired CL/Ps ranged from less than 3.5 per 100 000 population in Kerala and Goa to 10.9 per 100 000 population in Bihar (median rate, 5.9 [interquartile range, 4.6-7.3] per 100 000 population). CONCLUSIONS AND RELEVANCE: An estimated 72 000 cases of unrepaired CL/P are found in India. Poor states with less health care infrastructure have exceptionally high rates (eg, Bihar). These estimates are useful for informing international and national CL/P care strategies, allocating resources, and advocating for individuals and families affected by CL/P more broadly. LEVEL OF EVIDENCE: NA.