Occurrence of ochratoxin A in breast milk and urine samples of nursing mothers in Bangladesh.

The mycotoxin ochratoxin A (OTA) is a potent nephrotoxin with carcinogenic properties and, thus, of concern as a food contaminant. Since food contaminant data are scarce in Bangladesh, we applied human biomonitoring to gain more insights into OTA exposure in the country's population. OTA concentrations in human milk and urine samples of nursing mothers were determined with the aim to assess also exposure to this mycotoxin in breastfed infants. Breastfeeding mothers (n = 74) from three districts of Bangladesh (Sylhet, Cumilla, and Mymensingh region) participated in this study. They provided demographic data, along with breast milk and urine samples. OTA levels were measured by a competitive enzyme-linked immunosorbent assay (ELISA) with a detection limit of 60 ng/L for milk and 30 ng/L for urine.OTA was detected in 62.2% of all breast milk samples (mean 74.8 ± 49.0 ng/L, range < LOD-243.3 ng/L) and in 51.4% of all urine samples (mean 44.3 ± 63.5 ng/L, range < LOD-519.3 ng/L). The differences observed between regions for mean breast milk or for urinary OTA levels were relatively small. No significant correlation was observed between OTA levels in breast milk and food consumption patterns among nursing mothers. Regarding infant exposure, the estimated average daily intake of OTA for all was 15.0 ng/kg bw/day (range 4.5-45 ng/kg bw/day). In 34.5% of these infants, their estimated daily OTA intake exceeded a preliminary TDI value set by EFSA (17 ng/kg bw/day). The mean OTA intake was slightly higher (16.2 ± 7.8 ng/kg bw/day) in 1-2 months babies than in older infants (< 2 to 12 months), although the difference was not significant. Presence of OTA in most milk and urine samples of nursing mothers documents their widespread dietary mycotoxin exposure. Although based on a relatively small number of participants, the present analysis indicates non-negligible exposure of some nursed infants in Bangladesh. Therefore, further biomonitoring studies and investigations on major sources of OTA in food commodities are encouraged.

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.

Prevalence of preeclampsia and the associated risk factors among pregnant women in Bangladesh.

Preeclampsia is a multi-organ system disorder of pregnancy and is responsible for a significant rate of maternal morbidity and mortality worldwide. In Bangladesh, a large number of obstetric deaths occur every year but the exact reasons are not well investigated. The data regarding preeclampsia and its associated risk factors are scarce or limited in pregnant women in Bangladesh. Therefore, we aimed to conduct a cross-sectional study to estimate the prevalence of preeclampsia and identify the possible risk factors in a pregnant women cohort in Bangladesh. In this cross-sectional study, a total of 111 participants were enrolled and asked to include their anthropometric, socio-demographic, and other related lifestyle information in a standard questionnaire form. Blood samples were also collected from each participant to analyze serum levels of lipid profile, liver enzymes, uric acid, and creatinine by using standard methods. Logistic regression analysis was performed to identify the factors associated with preeclampsia. The overall prevalence of preeclampsia was 14.4%. About 10% of the pregnancies were found to have preeclampsia after 20 weeks of gestation without a previous history of hypertension. On the other hand, the prevalence of preeclampsia that superimposed on chronic hypertension was found to be 5.4%. Serum levels of TC, LDL-C, ALT and uric acid were significantly higher and HDL-C was significantly lower in preeclamptic pregnancies than the non-preeclamptic pregnancies. Respondents who required to take antihypertensive medications (AOR 5.45, 95% CI [1.09, 27.31]) and who never took antenatal care (AOR 6.83, 95% CI [1.00, 46.48]) were more likely to be preeclamptic. In conclusion, the present study showed a comparatively high prevalence of preeclampsia among pregnant women in Bangladesh. Some programmatic interventions such as medication for hypertension, antenatal visits to doctors, delivery and postnatal care services should be considered to reduce and prevent the hypertensive pregnancy disorders in Bangladesh.

Occurrence of aflatoxin M1 in human breast milk in Bangladesh.

Breast milk is the best, most complete form of nutrition for newborns and infants. However, human milk can contain aflatoxin M1 (AFM1) upon ingestion of dietary mycotoxin contaminants, namely, aflatoxin B1 (AFB1), by lactating mothers. AFB1 and its hydroxylated metabolite AFM1 are potent carcinogens and thus an important issue in food safety and public health. This study is the first to explore the presence of AFM1 in breast milk samples from Bangladesh and assess infant exposure to this toxin, as a consequence of maternal mycotoxin intake. A total of 62 breast milk samples were collected from nursing mothers in Sylhet region of Bangladesh. The milk samples were collected between October 2019 and March 2020 and analyzed by a sensitive enzyme-linked immunosorbent assay. AFM1 was detected in 51.6% of the breast milk samples (colostrum, transitional and mature milk), with a mean concentration of 4.42 ± 0.56 pg/mL, and in the range between LOD (4.0 pg/mL) and 6.66 pg/mL. The frequent detection of AFM1 in breast milk indicates widespread dietary exposure to mycotoxins in our cohort. The estimated average daily intake of AFM1 for all nursed infants was 0.49 ng/kg b.w./day. No significant correlations were observed between AFM1 levels in human milk and food items regularly consumed by nursing women. Overall, AFM1 levels in breast milk samples from the Sylhet region of Bangladesh are moderate, and lower than the permissible levels established for AFM1 in dairy milk or infant formulae (50 and 25 ng/kg, respectively). Yet, this first data for AFM1 breast milk contaminant levels just reflect the recent situation in one cohort, and monitoring should be continued.

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 ( KD=42nM ) 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.

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.

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.

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

A versatile and novel catalyst, CuO nanoparticles immobilized over functionalized mesoporous silica (nCuO-FMS), has been synthesized over an organically modified mesoporous silica framework following a facile synthetic route. The surface of the silica support (SBA-15) is first grafted with the 3-aminopropyl silane group and then further functionalized with tris(4-formylphenyl)amine. The reaction is performed in such a way that a few -CHO groups remain free for further functionalization. Finally, the CuO nanoparticles immobilized on mesoporous silica are obtained by a one pot reaction between the functionalized silica, 2-aminophenol and CuCl2. The product obtained has been used as a catalyst for the syntheses of symmetrical diselenides in the presence of KOH as the base and dimethyl sulphoxide (DMSO) as the solvent. The materials have been characterized thoroughly by X-ray powder diffraction, nitrogen adsorption-desorption studies, transmission electron microscopy, thermal analysis and different spectroscopic techniques. The Cu content of the sample has been determined by atomic absorption spectrophotometry (AAS). The products of the catalytic studies have been identified and estimated by NMR spectroscopy. Almost 78% isolated yield could be achieved at 363 K within 3 hours of the reaction and the catalyst, nCuO-FMS, can be recycled at least up to five catalytic cycles.

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

Mesoporous silica functionalized with a 2-hydroxy-naphthyl moiety has been synthesized and characterized by standard techniques like powder X-ray diffraction, N2 adsorption/desorption studies, transmission electron microscopy and spectral studies like FT-IR, UV-visible, fluorescence and 13C and 29Si solid state NMR. The functionalized silica material showed significant enhancement in its emission intensity in the presence of Al3+ ions whereas other metal ions could not bring about any increase in its emission intensity. They either quench the emission or do not alter the intensity significantly making the functionalized material a fluorescence chemosensor for Al3+. The sensitivity of the probe towards Al3+ has been determined to be high with a low limit of detection value. As functionalized silica is not soluble in common solvents, it has been effectively used to bind and remove Al3+ from a solution. Theoretical calculations on a model system have been performed to investigate the electronic spectral transitions.