Category: Nevin Manimala

Sci Rep. 2023 Jan 27;13(1):1530. doi: 10.1038/s41598-023-28479-2.

ABSTRACT

Tumour cells exhibit altered metabolic pathways that lead to radiation resistance and disease progression. Raman spectroscopy (RS) is a label-free optical modality that can monitor post-irradiation biomolecular signatures in tumour cells and tissues. Convolutional Neural Networks (CNN) perform automated feature extraction directly from data, with classification accuracy exceeding that of traditional machine learning, in cases where data is abundant and feature extraction is challenging. We are interested in developing a CNN-based predictive model to characterize clinical tumour response to radiation therapy based on their degree of radiosensitivity or radioresistance. In this work, a CNN architecture is built for identifying post-irradiation spectral changes in Raman spectra of tumour tissue. The model was trained to classify irradiated versus non-irradiated tissue using Raman spectra of breast tumour xenografts. The CNN effectively classified the tissue spectra, with accuracies exceeding 92.1% for data collected 3 days post-irradiation, and 85.0% at day 1 post-irradiation. Furthermore, the CNN was evaluated using a leave-one-out- (mouse, section or Raman map) validation approach to investigate its generalization to new test subjects. The CNN retained good predictive accuracy (average accuracies 83.7%, 91.4%, and 92.7%, respectively) when little to no information for a specific subject was given during training. Finally, the classification performance of the CNN was compared to that of a previously developed model based on group and basis restricted non-negative matrix factorization and random forest (GBR-NMF-RF) classification. We found that CNN yielded higher classification accuracy, sensitivity, and specificity in mice assessed 3 days post-irradiation, as compared with the GBR-NMF-RF approach. Overall, the CNN can detect biochemical spectral changes in tumour tissue at an early time point following irradiation, without the need for previous manual feature extraction. This study lays the foundation for developing a predictive framework for patient radiation response monitoring.

PMID:36707535 | DOI:10.1038/s41598-023-28479-2

Schizophrenia (Heidelb). 2023 Jan 27;9(1):6. doi: 10.1038/s41537-023-00332-5.

ABSTRACT

Smartphone technology provides us with a more convenient and less intrusive method of detecting changes in behavior and symptoms that typically precede schizophrenia relapse. To take advantage of the aforementioned, this study examines the feasibility of predicting schizophrenia relapse by identifying statistically significant anomalies in patient data gathered through mindLAMP, an open-source smartphone app. Participants, recruited in Boston, MA in the United States, and Bangalore and Bhopal in India, were invited to use mindLAMP for up to a year. The passive data (geolocation, accelerometer, and screen state), active data (surveys), and data quality metrics collected by the app were then retroactively fed into a relapse prediction model that utilizes anomaly detection. Overall, anomalies were 2.12 times more frequent in the month preceding a relapse and 2.78 times more frequent in the month preceding and following a relapse compared to intervals without relapses. The anomaly detection model incorporating passive data proved a better predictor of relapse than a naive model utilizing only survey data. These results demonstrate that relapse prediction models utilizing patient data gathered by a smartphone app can warn the clinician and patient of a potential schizophrenia relapse.

PMID:36707524 | DOI:10.1038/s41537-023-00332-5

Nat Commun. 2023 Jan 27;14(1):451. doi: 10.1038/s41467-023-36013-1.

ABSTRACT

The genetic regulation of post-prandial glucose levels is poorly understood. Here, we characterise the genetic architecture of blood glucose variably measured within 0 and 24 h of fasting in 368,000 European ancestry participants of the UK Biobank. We found a near-linear increase in the heritability of non-fasting glucose levels over time, which plateaus to its fasting state value after 5 h post meal (h² = 11%; standard error: 1%). The genetic correlation between different fasting times is > 0.77, suggesting that the genetic control of glucose is largely constant across fasting durations. Accounting for heritability differences between fasting times leads to a ~16% improvement in the discovery of genetic variants associated with glucose. Newly detected variants improve the prediction of fasting glucose and type 2 diabetes in independent samples. Finally, we meta-analysed summary statistics from genome-wide association studies of random and fasting glucose (N = 518,615) and identified 156 independent SNPs explaining 3% of fasting glucose variance. Altogether, our study demonstrates the utility of random glucose measures to improve the discovery of genetic variants associated with glucose homeostasis, even in fasting conditions.

PMID:36707517 | DOI:10.1038/s41467-023-36013-1

Environ Geochem Health. 2023 Jan 27. doi: 10.1007/s10653-023-01490-2. Online ahead of print.

ABSTRACT

This article proposes a mathematical model to characterize phytoremediation processes in soils contaminated with heavy metals. In particular, the proposed model constructs characteristic curves for the concentrations of several metals (As, Cd, Cu, Fe, Pb, Sb, and Zn) in soils and plants based on the experimental data retrieved from several bibliographical sources comprising 305 vegetal species. The proposed model is an extension of previous models of characteristic curves in phytoremediation processes developed by Lam et al. for root measurements using the bioconcentration factor. However, the proposed model extends this approach to consider roots, as well as aerial parts and shoots of the plant, while at the same time providing a less complex mathematical formula compared to the original. The final model shows an adjusted R2 of 0.712, and all its parameters are considered statistically significant. The model may be used to assess samples from a given plant species to identify its potential as an accumulator in the context of soil phytoremediation processes. Furthermore, a simplified version of the model was constructed using an approximation to provide an easy-to-compute alternative that is valid for concentrations below 37,000 mg/kg. This simplified model shows results similar to the original model for concentrations below this threshold and it uses an adjusted factor defined as [Formula: see text] that must be compared with a threshold depending on the metal, type of measurement, and target (e.g., accumulator or hyperaccumulator). The full model construction shows that 90 out of the 305 species assessed have a potential behavior as accumulators and 10 of them as hyperaccumulators. Finally, out of the 1405 experimental measurements, 1177 were shown to be accumulators or hyperaccumulators. In particular, 85% of the results coincide with the reported values, thus validating the proposed model.

PMID:36707498 | DOI:10.1007/s10653-023-01490-2

Environ Sci Pollut Res Int. 2023 Jan 28. doi: 10.1007/s11356-023-25545-0. Online ahead of print.

ABSTRACT

Each economic factor generates both positive and negative externalities regarding environmental quality. Owing to this, the current study aims to explore the impacts of various economic variables on the environmental quality of the Gulf Cooperation Council (GCC) region. By sampling the 24 years (1996-2019) financial statistics of six GCC region countries, we investigate the impact of economic growth, foreign investment, trade volume, tourism investment and revenue, and electricity production on CO₂ emissions. The empirical analysis is based upon dynamic least square and fully modified ordinary least square model due to the existence of cointegration. Following the results, economic growth, foreign investment, tourism investment, electricity production, and population density have a positive impact, while trade volume and banking development have a negative impact on the volume of CO₂ emissions. The results support the pollution haven hypothesis in the GCC region and have many policies for environmental economists regarding the protection of the natural environment in the long run. In parallel to economic growth, the policy officials from the GCC region should focus on environmental sustainability. They should exert more effort for developing sustainable economic growth policies. The current analysis offers new insights regarding the dynamic role of various economic factors in establishing the CO₂ emission volume in the GCC region.

PMID:36707478 | DOI:10.1007/s11356-023-25545-0

J Gen Intern Med. 2023 Jan 27. doi: 10.1007/s11606-022-07994-4. Online ahead of print.

ABSTRACT

BACKGROUND: Food insecurity has been associated with medication non-adherence among individuals living with chronic diseases like hypertension. The relationship between Supplemental Nutrition Assistance Program (SNAP)-a public program that addresses food insecurity-and Medication adherence among older Medicaid-insured adults living with hypertension is not clear.

OBJECTIVE: To analyze the association between patterns of SNAP participation and adherence to antihypertensive medications among older Medicaid-insured individuals.

DESIGN: Retrospective study using linked 2006-2014 state of Missouri’s Medicaid claims and Supplemental Nutrition Assistance Program data.

PARTICIPANTS: Older adults (≥ 60 years) who were continuously enrolled in Medicaid for 12 months following their first observed claim for hypertension at or after age 60.

MAIN MEASURES: The outcome measure was medication adherence assessed using the proportion of days covered (PDC). The exposure measures were as follows: (1) receipt of SNAP benefits (no [0], yes [1]); (2) SNAP benefits receipt during the 12-month Medicaid continuous enrollment (no [0], yes [1]); (3) duration of SNAP participation during the 12-month continuous Medicaid enrollment; and (4) SNAP participation pattern.

KEY RESULTS: On multivariable analyses, there was a statistically significant association between ever participating in SNAP and medication adherence (β = 0.32; S.E. = 0.011). Compared to those who participated in SNAP for 1-3 months during the 12-month continuous enrollment, there was an increased likelihood of medication adherence among those who were enrolled for 10-12 months (β = 0.44, S.E. = 0.041).

CONCLUSIONS: Medicaid-insured older adults who are SNAP participants or enrolled in SNAP for 10-12 months of a 12-month Medicaid continuous enrollment period are more likely to be adherent to antihypertensive medication compared to non-SNAP participants or those enrolled for 1-3 months, respectively.

PMID:36707458 | DOI:10.1007/s11606-022-07994-4

J Pharmacokinet Pharmacodyn. 2023 Jan 27. doi: 10.1007/s10928-022-09840-w. Online ahead of print.

ABSTRACT

Determining a drug dosing recommendation with a PKPD model can be a laborious and complex task. Recently, an optimal dosing algorithm (OptiDose) was developed to compute the optimal doses for any pharmacometrics/PKPD model for a given dosing scenario. In the present work, we reformulate the underlying optimal control problem and elaborate how to solve it with standard commands in the software NONMEM. To demonstrate the potential of the OptiDose implementation in NONMEM, four relevant but substantially different optimal dosing tasks are solved. In addition, the impact of different dosing scenarios as well as the choice of the therapeutic goal on the computed optimal doses are discussed.

PMID:36707456 | DOI:10.1007/s10928-022-09840-w

Int J Legal Med. 2023 Jan 28. doi: 10.1007/s00414-023-02953-y. Online ahead of print.

ABSTRACT

The biological aspects of determining the dental age of subadults represent an important interdisciplinary scientific link with applications in criminal law and in forensic anthropology and dentistry. In criminal procedural law, it is necessary to determine the exact age of an undocumented person in view of the application of the provisions on juvenile offenders and minor victims. Chronological age can be estimated from the development of the third molars, as these are the only teeth that develop at the age of 18. The aim of this study was to verify the applicability of the Gambier method based on the eruption of the third permanent molars in the mandible and maxilla, to contribute to forensic age assessment. The analyzed group that met the criteria consisted of 811 orthopantomograms (OPGs) (339 females and 472 males) between the ages of 13 and 25 years. The OPGs were retrospectively analyzed according to the method of Gambier et al. (Int J Legal Med 133:625-632, 29), which refers to the eruption stages of the third molar. Differences between eruption stages of maxillary and mandibular third molars were statistically significant in both biological sexes. Intersexual differences in mean age were significant only at stage 3 for any M3 tooth and at stage 1 for mandibular M3. There were no statistically significant differences between the left and right mandibular and maxillary third molars, respectively. Differences between mandibular and maxillary M3 were significant only for stage 1 in males on the left side and for stage 2 in both sexes and sides. The method used allowed the best classification of individuals into minor and adult groups (based on phase D-90.41% of individuals, based on the third stage of the mandibular left third molar-86.29%). Based on the results obtained, we can assume that the method cannot be used alone in the initial examination of living individuals, since all four third molars must be assessed and there are no additional findings from practice.

PMID:36707450 | DOI:10.1007/s00414-023-02953-y

J Cancer Res Clin Oncol. 2023 Jan 28. doi: 10.1007/s00432-022-04562-5. Online ahead of print.

ABSTRACT

PURPOSE: The current standard of care for chronic-phase chronic myeloid leukemia (CP-CML) is tyrosine kinase inhibitors (TKIs). Treatment recommendations are unclear for CP-CML failing ≥ 2 lines of treatment, partly due to the paucity of head-to-head trials evaluating TKIs. Thus, matching-adjusted indirect comparisons (MAICs) were conducted to compare asciminib with competing TKIs in third- or later line (≥ 3L) CP-CML.

METHODS: Individual patient-level data for asciminib (ASCEMBL; follow-up: ≥ 48 weeks) and published aggregate data for comparator TKIs (ponatinib, nilotinib, and dasatinib) informed the analyses. Major molecular response (MMR), complete cytogenetic response (CCyR), and time to treatment discontinuation (TTD) were assessed, where feasible.

RESULTS: Asciminib was associated with statistically significant improvements in MMR by 6 (relative risk [RR]: 1.55; 95% confidence interval [CI]: 1.02, 2.36) and 12 months (RR: 1.48; 95% CI: 1.03, 2.14) vs ponatinib. For CCyR, the results vs ponatinib were similar by 6 (RR: 1.11; 95% CI: 0.81, 1.52) and 12 months (RR: 0.97; 95% CI: 0.73, 1.28). Asciminib was associated with improvements in MMR by 6 months vs dasatinib but with a CI overlapping one (RR 1.52; 95% CI: 0.66, 3.53). Asciminib was associated with statistically significant improvements in CCyR by 6 (RR: 3.57; 95% CI: 1.42, 8.98) and 12 months (RR: 2.03; 95% CI: 1.12, 3.67) vs nilotinib/dasatinib. Median TTD was unreached for asciminib in ASCEMBL. However, post-adjustment asciminib implied prolonged TTD vs nilotinib and dasatinib, but not vs ponatinib.

CONCLUSION: These analyses demonstrate favorable outcomes with asciminib versus competing TKIs, highlighting its therapeutic potential in ≥ 3L CP-CML.

PMID:36707445 | DOI:10.1007/s00432-022-04562-5

Immunogenetics. 2023 Jan 28. doi: 10.1007/s00251-023-01296-7. Online ahead of print.

ABSTRACT

Human leukocyte antigen (HLA) class I and II loci are essential elements of innate and acquired immunity. Their functions include antigen presentation to T cells leading to cellular and humoral immune responses, and modulation of NK cells. Their exceptional influence on disease outcome has now been made clear by genome-wide association studies. The exons encoding the peptide-binding groove have been the main focus for determining HLA effects on disease susceptibility/pathogenesis. However, HLA expression levels have also been implicated in disease outcome, adding another dimension to the extreme diversity of HLA that impacts variability in immune responses across individuals. To estimate HLA expression, immunogenetic studies traditionally rely on quantitative PCR (qPCR). Adoption of alternative high-throughput technologies such as RNA-seq has been hampered by technical issues due to the extreme polymorphism at HLA genes. Recently, however, multiple bioinformatic methods have been developed to accurately estimate HLA expression from RNA-seq data. This opens an exciting opportunity to quantify HLA expression in large datasets but also brings questions on whether RNA-seq results are comparable to those by qPCR. In this study, we analyze three classes of expression data for HLA class I genes for a matched set of individuals: (a) RNA-seq, (b) qPCR, and (c) cell surface HLA-C expression. We observed a moderate correlation between expression estimates from qPCR and RNA-seq for HLA-A, -B, and -C (0.2 ≤ rho ≤ 0.53). We discuss technical and biological factors which need to be accounted for when comparing quantifications for different molecular phenotypes or using different techniques.

PMID:36707444 | DOI:10.1007/s00251-023-01296-7