Using artificial intelligence to learn optimal regimen plan for Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is a progressive neurological disorder with no specific curative medications. Sophisticated clinical skills are crucial to optimize treatment regimens given the multiple coexisting comorbidities in the patient population.

OBJECTIVE

Here, we propose a study to leverage reinforcement learning (RL) to learn the clinicians' decisions for AD patients based on the longitude data from electronic health records.

METHODS

In this study, we selected 1736 patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We focused on the two most frequent concomitant diseases-depression, and hypertension, thus creating 5 data cohorts (ie, Whole Data, AD, AD-Hypertension, AD-Depression, and AD-Depression-Hypertension). We modeled the treatment learning into an RL problem by defining states, actions, and rewards. We built a regression model and decision tree to generate multiple states, used six combinations of medications (ie, cholinesterase inhibitors, memantine, memantine-cholinesterase inhibitors, hypertension drugs, supplements, or no drugs) as actions, and Mini-Mental State Exam (MMSE) scores as rewards.

RESULTS

Given the proper dataset, the RL model can generate an optimal policy (regimen plan) that outperforms the clinician's treatment regimen. Optimal policies (ie, policy iteration and Q-learning) had lower rewards than the clinician's policy (mean -3.03 and -2.93 vs. -2.93, respectively) for smaller datasets but had higher rewards for larger datasets (mean -4.68 and -2.82 vs. -4.57, respectively).

CONCLUSIONS

Our results highlight the potential of using RL to generate the optimal treatment based on the patients' longitude records. Our work can lead the path towards developing RL-based decision support systems that could help manage AD with comorbidities.

Leveraging multi-source to resolve inconsistency across pharmacogenomic datasets in drug sensitivity prediction

Pharmacogenomics datasets have been generated for various purposes, such as investigating different biomarkers. However, when studying the same cell line with the same drugs, differences in drug responses exist between studies. These variations arise from factors such as inter-tumoral heterogeneity, experimental standardization, and the complexity of cell subtypes. Consequently, drug response prediction suffers from limited generalizability. To address these challenges, we propose a computational model based on Federated Learning (FL) for drug response prediction. By leveraging three pharmacogenomics datasets (CCLE, GDSC2, and gCSI), we evaluate the performance of our model across diverse cell line-based databases. Our results demonstrate superior predictive performance compared to baseline methods and traditional FL approaches through various experimental tests. This study underscores the potential of employing FL to leverage multiple data sources, enabling the development of generalized models that account for inconsistencies among pharmacogenomics datasets. By addressing the limitations of low generalizability, our approach contributes to advancing drug response prediction in precision oncology.

Using Artificial Intelligence to Learn Optimal Regimen Plan for Alzheimer's Disease

Background

Alzheimer's Disease (AD) is a progressive neurological disorder with no specific curative medications. While only a few medications are approved by FDA (i.e., donepezil, galantamine, rivastigmine, and memantine) to relieve symptoms (e.g., cognitive decline), sophisticated clinical skills are crucial to optimize the appropriate regimens given the multiple coexisting comorbidities in this patient population.

Objective

Here, we propose a study to leverage reinforcement learning (RL) to learn the clinicians' decisions for AD patients based on the longitude records from Electronic Health Records (EHR).

Methods

In this study, we withdraw 1,736 patients fulfilling our criteria, from the Alzheimer's Disease Neuroimaging Initiative(ADNI) database. We focused on the two most frequent concomitant diseases, depression, and hypertension, thus resulting in five main cohorts, 1) whole data, 2) AD-only, 3) AD-hypertension, 4) AD-depression, and 5) AD-hypertension-depression. We modeled the treatment learning into an RL problem by defining the three factors (i.e., states, action, and reward) in RL in multiple strategies, where a regression model and a decision tree are developed to generate states, six main medications extracted (i.e., no drugs, cholinesterase inhibitors, memantine, hypertension drugs, a combination of cholinesterase inhibitors and memantine, and supplements or other drugs) are for action, and Mini-Mental State Exam (MMSE) scores are for reward.

Results

Given the proper dataset, the RL model can generate an optimal policy (regimen plan) that outperforms the clinician's treatment regimen. With the smallest data samples, the optimal-policy (i.e., policy iteration and Q-learning) gained a lesser reward than the clinician's policy (mean -2.68 and -2.76 vs . -2.66, respectively), but it gained more reward once the data size increased (mean -3.56 and -2.48 vs . -3.57, respectively).

Conclusions

Our results highlight the potential of using RL to generate the optimal treatment based on the patients' longitude records. Our work can lead the path toward the development of RL-based decision support systems which could facilitate the daily practice to manage Alzheimer's disease with comorbidities.

Abstract 1223: Simultaneous knockdown of CD320 and LRP2 receptors is selectively toxic to cancer cells but not normal cells

Designing effective therapies with selective toxicity in cancer cells is a priority of cancer research. Porphyrins are known to selectively accumulate in cancerous tissue, a feature that has been exploited in cancer diagnostics and therapeutics. The porphyrin TCPP is used in the CyPath® Lung test for diagnosis of lung cancer. During efforts to elucidate the mechanism by which TCPP selectively accumulates in cancer cells, we discovered that simultaneous knockdown of two LDLR-family receptors, CD320 and LRP2, was selectively cytotoxic to cancer cells and not to normal cells. CD320 is a receptor that internalizes the complex of cobalamin (Vitamin B12)/transcobalamin II. LRP2 is a receptor strongly expressed in the kidney which also internalizes the cobalamin/transcobalamin II complex, in addition to more than 50 other ligands. Cobalamin is the catalytic cofactor for the enzymes methionine synthase and methylmalonyl CoA mutase, which play direct and indirect roles in folate metabolism, nucleotide biosynthesis, DNA methylation, and metabolism of fatty acids and amino acids, which suggests that cobalamin deprivation might be particularly deleterious to metabolically active cancer cells. We designed siRNAs to effectively silence CD320 and LRP2 protein expression and transfected these siRNAs into normal and cancer cell lines using commercial transfection reagents. Effective gene knockdown (up to 90%) was confirmed by western blotting. Simultaneous siRNA knockdown of CD320 and LRP2 in normal fibroblasts did not affect their cell proliferation, as measured by the Cell Titer-GLO assay. However, knockdown of CD320 and LRP2 in cancer cell lines derived from diverse tissues (lung, breast, prostate, brain, and skin cancers) inhibited cell proliferation or killed the cells by up to 80%. Interestingly, in some cell lines, when either CD320 or LRP2 were silenced individually, a concurrent increase in protein expression of the other receptor was observed, suggesting that CD320 and LRP2 compensate for each other's function; hence, silencing both receptors is required for optimal cell killing. Ongoing studies will test the hypothesis that cobalamin deficiency is a mechanism for the selective cytotoxicity observed in cancer cells. Further studies include in vivo animal studies to study the effects on CD320 and LRP2 knockdown on human tumor xenograft models. In summary, we have found that inhibiting CD320 and LRP2 expression strongly and selectively inhibits cancer cell growth, leading to a promising therapeutic strategy for diverse cancers.

Citation Format: David J. Elzi, William Bauta, Shao-Chiang Lai, Trisha Das, Shweta Mogare, Vivienne I. Rebel. Simultaneous knockdown of CD320 and LRP2 receptors is selectively toxic to cancer cells but not normal cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1223.

Identification of a novel mechanism for meso-tetra (4-carboxyphenyl) porphyrin (TCPP) uptake in cancer cells

Porphyrins are used for cancer diagnostic and therapeutic applications, but the mechanism of how porphyrins accumulate in cancer cells remains elusive. Knowledge of how porphyrins enter cancer cells can aid the development of more accurate cancer diagnostics and therapeutics. To gain insight into porphyrin uptake mechanisms in cancer cells, we developed a flow cytometry assay to quantify cellular uptake of meso-tetra (4-carboxyphenyl) porphyrin (TCPP), a porphyrin that is currently being developed for cancer diagnostics. We found that TCPP enters cancer cells through clathrin-mediated endocytosis. The LDL receptor, previously implicated in the cellular uptake of other porphyrins, only contributes modestly to uptake. We report that TCPP instead binds strongly ( K D = 42 nM ) to CD320, the cellular receptor for cobalamin/transcobalamin II (Cbl/TCN2). Additionally, TCPP competes with Cbl/TCN2 for CD320 binding, suggesting that CD320 is a novel receptor for TCPP. Knockdown of CD320 inhibits TCPP uptake by up to 40% in multiple cancer cell lines, including lung, breast, and prostate cell lines, which supports our hypothesis that CD320 both binds to and transports TCPP into cancer cells. Our findings provide some novel insights into why porphyrins concentrate in cancer cells. Additionally, our study describes a novel function for the CD320 receptor which has been reported to transport only Cbl/TCN2 complexes.

Supercapacitor behaviour of manganese dioxide decorated mesoporous silica synthesized by a rapid sol-gel inverse micelle method

A new type of mesoporous silica (MS) with high surface area and large pore volume has been synthesised by employing a rapid sol-gel based inverse micelle method and electrochemically active metal center, manganese, has been incorporated into it. The MnO2 decorated silica composites are obtained through the wet impregnation technique using KMnO4 followed by their reduction under neutral conditions. The structure and surface area of the samples have been characterised by powder X-ray diffraction (XRD), BET surface area and pore size analysis, transmission and scanning electron microscopy (TEM and FE-SEM), FT-IR spectroscopy and X-ray photoelectron spectroscopy (XPS). Electrochemical techniques, i.e. cyclic voltammetry (CV), galvanostatic charge-discharge (GCD) and electrochemical impedance spectroscopy (EIS), have been used to evaluate the electrochemical properties of the composites. The resultant composite MS/MnO2-3 with a significantly high surface area (453 m2 g-1) is found to exhibit a superior specific capacitance of 1158.50 F g-1 at a scan rate of 5 mV s-1 which is very close to the theoretical value and retains 87.8% of its capacitance up to 1000 cycles at 1 A g-1 current density. The outstanding electrochemical performance of the composite can be attributed to the high surface area and uniform pore size distribution of the novel silica host which simultaneously increases the electrochemically active centres, promotes electrolyte penetration and enhances electron transport. The simplicity of the synthesis process developed here is interesting for wide-scale production of MnO2-based electro-active materials.

Anaerobic co-digestion of sewage sludge with cellulose, protein, and lipids: Role of rheology and digestibility

Rheology is known to have an impact on the performance of digesters, but the effect of additional substrates (co-digestion) is poorly understood. The main objective of this study was to investigate the effects of the addition of cellulose, protein and lipids to substrates on the rheological behaviour and biogas production of the mixture of primary sludge (PS) and waste-activated sludge (WAS) in a batch system. A mixture of PS and WAS to form the main substrate was anaerobically co-digested with different types of organic matter (cellulose, protein and lipids) as co-substrates at different co-substrate to main substrate ratios of 2-8 (wt%) under mesophilic conditions and below ammonia inhibition levels. Yield stress (τ_y) and the flow consistency index (k) of the combined feed in the case of cellulose and protein were significantly dependent on the amount of co-substrate added, while there was an insignificant impact on these properties when lipids were added. Cellulose significantly increased τ_y and k in the feed, which resulted in poor fluidity and the improper homogenisation of the digester content, and consequently decreased the biogas yield. In contrast, the biogas yield was improved through the addition of 2% to 6% protein despite an increase in τ_y and k of the feed, but the methane yield decreased at 7% and 8% levels of protein concentration. This observation indicates that the threshold for τ_y and k of the digester media depends on the organic nature and digestibility of the substrate. There was no significant impact on the flow properties of the initial mixture when lipids were added, and their addition increased the biogas yield. A first-order kinetic reaction model was used for predicting the yield of methane from these digesters. The rate constant values revealed an increasing trend, with the highest for protein then lipids then cellulose.

The big effect of a small change: formation of CuO nanoparticles instead of covalently bound Cu(ii) over functionalized mesoporous silica and its impact on catalytic efficiency

Two different heterogeneous catalysts, one with Cu(ii) covalently bonded to functionalized mesoporous silica (FMS-Cu(II)) and another with CuO nanoparticles immobilized over the same silica (FMS-CuO-np), have been synthesized by a common route but with a minor alteration in the sequence of addition of reagents. It is interesting to find that by merely changing the order of the addition of reagents Cu(ii) can be incorporated into the framework in two different forms. In one case Cu(ii) binds to the N and O donor centers present in the functionalized material whereas in the other case CuO nanoparticles are generated in situ. The materials have been thoroughly characterized by powder X-ray diffraction, nitrogen adsorption/desorption, transmission electron microscopy, thermal analysis, FT-IR spectroscopy, solid state MAS-NMR spectroscopy and atomic absorption spectrophotometric studies. The synthesized products have been examined for their catalytic efficiencies in the oxidation of olefins, as a model case. Styrene, α-methyl styrene, cyclohexene, trans-stilbene and cyclooctene have been used as substrates in the presence of tert-butyl hydroperoxide as the oxidant in acetonitrile medium under mild conditions. The products of the catalytic reactions have been identified and estimated by gas chromatography and gas chromatography-mass spectrometry. The rate of conversion of the substrates for both the catalysts is high and the selectivity is also good. But from comparative studies, it is found that FMS-CuO-np which contains CuO nanoparticles shows better efficiency than FMS-Cu(II). The catalysts have been recycled for five catalytic cycles without showing much decrease in their catalytic activity.

The ORNATE India Project: United Kingdom-India Research Collaboration to tackle visual impairment due to diabetic retinopathy

INTRODUCTION

The ORNATE India project is funded by the UK Research and Innovation (UKRI) through the Global Challenges Research Fund. The aim is to build research capacity and capability in India and the UK to tackle global burden of diabetes-related visual impairment. As there are over 77 million people with diabetes in India, it is challenging to screen every person with diabetes annually for sight-threatening diabetic retinopathy (DR). Therefore, alternate safe approaches need to be developed so that those at-risk of visual impairment due to DR is identified promptly and treated.

METHODS

The project team utilised diverse global health strategies and research methods to co-design work packages to build research capacity and capability to ensure effective, affordable and efficient DR services are made available for the population. The strategies and methods employed included health system strengthening; implementation science; establishing care pathways; co-designing collaborative studies on affordable technologies, developing quality standards and guidelines to decrease variations in care; economic analysis; risk modelling and stratification. Five integrated work packages have been developed to deal with all aspects of DR care. These included implementation of a DR screening programme in the public health system in a district in Kerala, evaluating regional prevalence of diabetes and DR and assessing ideal tests for holistic screening for diabetes and its complications in 20 areas in India, utilising artificial intelligence on retinal images to facilitate DR screening, exploring biomarker and biosensor research to detect people at risk of diabetes complications, estimating cost of blindness in India and risk modelling to develop risk-based screening models for diabetes and its complications. A large collaborative network will be formed to propagate research, promote shared learning and bilateral exchanges between high- and middle-income countries to tackle diabetes-related blindness.

STELAR: a statistically consistent coalescent-based species tree estimation method by maximizing triplet consistency

Background

Species tree estimation is frequently based on phylogenomic approaches that use multiple genes from throughout the genome. However, estimating a species tree from a collection of gene trees can be complicated due to the presence of gene tree incongruence resulting from incomplete lineage sorting (ILS), which is modelled by the multi-species coalescent process. Maximum likelihood and Bayesian MCMC methods can potentially result in accurate trees, but they do not scale well to large datasets.

Results

We present STELAR (Species Tree Estimation by maximizing tripLet AgReement), a new fast and highly accurate statistically consistent coalescent-based method for estimating species trees from a collection of gene trees. We formalized the constrained triplet consensus (CTC) problem and showed that the solution to the CTC problem is a statistically consistent estimate of the species tree under the multi-species coalescent (MSC) model. STELAR is an efficient dynamic programming based solution to the CTC problem which is highly accurate and scalable. We evaluated the accuracy of STELAR in comparison with SuperTriplets, which is an alternate fast and highly accurate triplet-based supertree method, and with MP-EST and ASTRAL – two of the most popular and accurate coalescent-based methods. Experimental results suggest that STELAR matches the accuracy of ASTRAL and improves on MP-EST and SuperTriplets.

Conclusions

Theoretical and empirical results (on both simulated and real biological datasets) suggest that STELAR is a valuable technique for species tree estimation from gene tree distributions.

Mesoporous silica based recyclable probe for colorimetric detection and separation of ppb level Hg2+ from aqueous medium

Functional mesoporous silica probes, MCM-TFM and SBA-TFM, have been synthesized with varying pore sizes and having S-donor sites judiciously selected to bind soft metal centers. The soft S-donor centers are contributed by the thiol functional groups that are introduced into the silica matrices by functionalization with tris(4-formylphenyl)amine followed by 2-aminothiophenol. The materials rapidly and selectively detect Hg2+ colorimetrically and the change in color profile can be perceived through bare eyes. The probes can decontaminate the pollutant heavy metal from aqueous medium at ppb level and the materials are recyclable and reusable for several separation cycles.

Immunohistochemical detection of naturally occurring porcine Sapelovirus infection in Indian pigs

We investigated immunohistochemical detection of porcine Sapelovirus (PSV) in naturally infected pigs of different ages. Forty-nine fecal samples, intestinal contents and other tissue samples from dead pigs were screened in previous study using reverse transcription polymerase chain reaction (RT-PCR) for PSV infection. Eight animals were positive for PSV based on RT-PCR examination. Gross lesions were recorded mainly in the large and small intestines. Microscopic examination of intestines showed severe enteritis. Tissue sections of all organs from PSV positive animals were immunostained using hyperimmune serum raised in rats against PSV that had been grown in a BHK-21 cell line. Staining of PSV was found only in the large and small intestines.

Impact of rheological properties of substrate on anaerobic digestion and digestate dewaterability: New insights through rheological and physico-chemical interaction

Mesophilic batch anaerobic digesters fed by different substrates were set up to identify the role of substrate rheology in anaerobic digestion performance while operating below the toxic level. Five substrates of different rheological behaviour but at the same amount of organic matters were prepared by addition of different amount of an inert material (0, 0.03, 0.07, 0.11, and 0.20 g) per g of waste activated sludge (WAS). To gain a comprehensive insight, the interactive relationship between substrate rheology, physico-chemical properties and biogas production as well as digestate dewaterability was investigated. The results proved that better access of microorganisms to organic matters improved the digester performance and led to 19.29% and 12.5% increase in biogas yield and VS removal efficiency, respectively. Moreover, the statistical analysis showed that consistency index and loss modulus of sludge could be employed as promising indications for biogas yield while yield stress could predict dewaterability of digestate as far as the other physico-chemical properties remained unchanged. During digestion measurement of consistency index and loss modulus of digestate could be performed as a reliable tool to monitor biogas production.

Rhodamine functionalized mesoporous silica as a chemosensor for the efficient sensing of Al3+, Cr3+ and Fe3+ ions and their removal from aqueous media

Rhodamine incorporated mesoporous silica acts as a selective chemosensor for Al³⁺, Cr³⁺ and Fe³⁺ ions and it is used for their separation from an aqueous medium.

Recommended electron-impact excitation and ionization cross sections for Be I

Analytic fits to the recommended electron-impact excitation and ionization cross sections for Be I are presented. The lowest 19 terms of configurations 2snl (n ≤ 4) and 2p ² terms below the first ionization limit are considered. The fits are based on the accurate calculations with the convergent close coupling (CCC) method as well as the B-spline R-matrix (BSR) approach. The fitted cross sections provide rate coefficients that are believed to approximate the original data within 10% with very few exceptions. The oscillator strengths for the dipole-allowed transitions between all the considered states are calculated with the relativistic multi-configuration Dirac-Hartree-Fock (MCDHF) approach and compared with the CCC and BSR results. This comparison shows a very good agreement except for a handful of cases with likely strong cancellations.

In situ synthesis of CuO nanoparticles over functionalized mesoporous silica and their application in catalytic syntheses of symmetrical diselenides

A versatile and novel mesoporous silica supported CuO nanoparticle catalyst (nCuO-FMS) and its application in the syntheses of symmetrical diselenides.

Major venom proteins of the fire ant Solenopsis invicta: insights into possible pheromone-binding function from mass spectrometric analysis

Proteins in the venom of the fire ant Solenopsis invicta have been suggested to function in pheromone binding. Venom from queens and workers contains different isoforms of these proteins, consistent with the differing pheromones they secrete, but questions remain about the venom protein composition and glandular source. We found that the queen venom contains a previously uncharacterized pheromone-binding protein paralogue known as Sol i 2X1. Using imaging mass spectrometry, we located the main venom proteins in the poison sac, implying that pheromones might have to compete with venom alkaloids for binding. Using the known structure of the worker venom protein Sol i 2w, we generated three-dimensional homology models of the worker venom protein Sol i 4.02, and of the two main venom proteins in queens and female alates, Sol i 2q and Sol i 2X1. Surprisingly, the models show that the proteins have relatively small internal hydrophobic binding pockets that are blocked by about 10 amino acids of the C-terminal region. For these proteins to function as carriers of hydrophobic ligands, a conformational change would be required to displace the C-terminal region, somewhat like the mechanism known to occur in the silk moth pheromone-binding protein.

Continuous Hyperfractionated Accelerated Radiotherapy (CHART) for Non-small Cell Lung Cancer (NSCLC): 7 Years' Experience From Nine UK Centres

AIM

Continuous hyperfractionated accelerated radiotherapy (CHART) remains an option to treat non-small cell lung cancer (NSCLC; NICE, 2011). We have previously published treatment outcomes from 1998-2003 across five UK centres. Here we update the UK CHART experience, reporting outcomes and toxicities for patients treated between 2003 and 2009.

MATERIALS AND METHODS

UK CHART centres were invited to participate in a retrospective data analysis of NSCLC patients treated with CHART from 2003 to 2009. Nine (of 14) centres were able to submit their data into a standard database. The Kaplan-Meier method estimated survival and the Log-rank test analysed the significance.

RESULTS

In total, 849 patients had CHART treatment, with a median age of 71 years (range 31-91), 534 (63%) were men, 55% had undergone positron emission tomography-computed tomography (PET-CT) and 26% had prior chemotherapy; 839 (99%) patients received all the prescribed treatment. The median overall survival was 22 months with 2 and 3 year survival of 47% and 32%, respectively. Statistically significant differences in survival were noted for stage IA versus IB (33.2 months versus 25 months; P = 0.032) and IIIA versus IIIB (20 months versus 16 months; P = 0.018). Response at 3 months and outcomes were significantly linked; complete response showing survival of 34 months against 19 months, 15 months and 8 months for partial response, stable and progressive disease, respectively (P < 0.001). Age, gender, performance status, prior chemotherapy and PET-CT did not affect the survival outcomes. Treatment was well tolerated with <5% reporting ≥grade 3 toxicity.

CONCLUSION

In routine practice, CHART results for NSCLC remain encouraging and we have been able to show an improvement in survival compared with the original trial cohort. We have confirmed that CHART remains deliverable with low toxicity rates and we are taking a dose-escalated CHART regimen forward in a randomised phase II study of sequential chemoradiotherapy against other accelerated dose-escalated schedules.

Characterization and compositional analysis of highly acidic karanja oil and its potential feedstock for enzymatic synthesis of biodiesel

In the present study, we focused on the synthesis of biodiesel fromPseudomonas cepacia, crude karanja oil by the process of enzymatic transesterification using bio-support materials, such as lipase immobilized on polyvinyl alcohol/AlgNa.

Pronounced effect of pore dimension of silica support on Pd-catalyzed Suzuki coupling reaction under ambient conditions

The pore size of the mesoporous silica support affects the reactivity of heterogeneous palladium catalysts in the Suzuki reaction under ambient conditions, which is leveled at higher temperature.

Clinical utility of a short resting-state MRI scan in differentiating bipolar from unipolar depression

OBJECTIVE

Depression in bipolar disorder (BipD) requires a therapeutic approach that is from treating unipolar major depressive disorder (UniD), but to date, no reliable methods could separate these two disorders. The aim of this study was to establish the clinical validity and utility of a non-invasive functional MRI-based method to classify BipD from UniD.

METHOD

The degree of connectivity (degree centrality or DC) of every small unit (voxel) with every other unit of the brain was estimated in 22 patients with BipD and 22 age, gender, and depressive severity-matched patients with UniD and 22 healthy controls. Pattern classification analysis was carried out using a support-vector machine (SVM) approach.

RESULTS

Degree centrality pattern from 8-min resting fMRI discriminated BipD from UniD with an accuracy of 86% and diagnostic odds ratio of 9.6. DC was reduced in the left insula and increased in bilateral precuneus in BipD when compared to UniD. In this sample with a high degree of uncertainty (50% prior probability), positive predictive value of the DC test was 79%.

CONCLUSION

Degree centrality maps are potential candidate measures to separate bipolar depression from unipolar depression. Test performance reported here requires further pragmatic evaluation in regular clinical practice.

Outcome of universal newborn eye screening with wide-field digital retinal image acquisition system: a pilot study

PurposeTo evaluate the outcome of universal newborn eye screening with wide-field digital retinal imaging (WFDRI) system.MethodsIn this pilot study, we examined 1152 apparently healthy newborn infants in the obstetrics and gynecology ward of a civil hospital in Eastern India over 1.5 years. The examination included external eye examination, red reflex test and fundus imaging by WFDRI (RetCam II, Clarity medical system, Pleasanton, CA, USA) by a trained optometrist. The pathologies detected, net monetary gain and skilled manpower saved were documented. The results were compared with three similar studies thus far published in the literature.ResultsOcular abnormality of any kind was seen in 172 (14.93%) babies. Retinal hemorrhage in 153 babies (88.9% of all abnormal findings) was the most common abnormality; it was bilateral in 118 (77.12%) babies and 4 babies had foveal hemorrhage. Other abnormalities included vitreous hemorrhage (n=1), congenital glaucoma (n=2), uveal coloboma (n=2), retinopathy mimicking retinopathy of prematurity (n=2), and cystic fovea (n=3). The retinal hemorrhages resolved spontaneously in all eyes. One baby with congenital glaucoma received surgery and the other was treated medically. The benefits included savings in skilled manpower, a net monetary gain of INR 4.195 million (US$ 62,612) and skilled manpower saving by 319.4 h.ConclusionsThe universal neonatal eye screening using WFDRI detected pathologies that needed immediate care or regular follow up; saved skilled manpower with a net monetary gain. But compared to a red reflex test the benefits were marginal in terms of detecting treatment warranting ocular pathologies.

Frequency, Distribution of Congenital Anomaly and Associated Maternal Risk Factors

This study was done to find out the maternal risk factors associated with congenital anomaly. This cross-sectional observational study was carried out in the Department of Obstetrics and Gynaecology, Bangabandhu Sheikh Mujib Medical University (BSMMU) Hospital, Dhaka, Bangladesh from January 2011 to December 2011. During this study period 78 patients had pregnancy with congenital anomaly and delivered in the Department of Obstetrics and Gynaecology. Women with ultrasound report of congenitally abnormal fetus irrespective of gestational age were included. Clinical evaluation of neonates was done by experienced neonatologist. The frequency of congenital anomaly was 3.46%. Most of the women belong to age <35 years (97.43%). Congenital anomalies more commonly were seen in the primiparas (64.10%). Most frequent associated risk factor was the history of abortions (35.89%). Maternal infections during antenatal period were also high (15.58%). There were 58 males (74.35%) and 20 females (25.64%). There was positive history of delivery of congenital abnormal babies in 6 cases (7.6%). Mothers of eight cases (10.25%) had history of drug ingestion during pregnancy. Four cases (5.12%) of mothers had hypothyroidism and 6 cases of mothers (7.69%) had diabetes mellitus respectively.

2-Hydroxy-naphthyl functionalized mesoporous silica for fluorescence sensing and removal of aluminum ions

2-Hydroxy-naphthyl functionalized mesoporous silica has been used as a turn-on fluorescent chemosensor for Al³⁺ and used to remove it with high efficiency.

Medication Adherence in Kidney Transplant Recipients in an Urban Indian Setting

Medication nonadherence is a known problem after renal transplantation and can vary from one setting to another. Since it can lead to negative outcomes, it is important to develop intervention strategies to enhance adherence in a given setting using determinants identified through exploratory studies. We explored nonadherence in renal transplant recipients. A longitudinal survey was done with adult renal transplant recipients at a tertiary care public and two private hospitals of Kolkata. Subjects were followed-up for 1 year. After screening for medication adherence status by the four-item Morisky Medication Adherence Scale, those admitting to potential nonadherence were probed further. A patient was deemed to be nonadherent if failing to take medicines on appointed time (doses missed or delayed by more than 2 h) more than three times in any month during the observation period. A pretested questionnaire was used to explore potential determinants of nonadherence. Data of 153 patients recruited over a 2-year were analyzed. The extent of nonadherence with immunosuppressant regimens was about 31% overall; 44% in the public sector and 19% in the private sector (P < 0.001). Nonadherence with other medication was around 19% in both the sectors. Several potential demographic, socioeconomic and psychosocial determinants of nonadherence were identified on univariate analysis. However, logistic regression analysis singled out only the economic status. This study had updated the issue of nonadherence in renal transplant recipients in the Indian setting. Strategies to improve medication adherence can be planned by relevant stakeholders on the basis of these findings.