Tag: nevin manimala

Sci Rep. 2023 Apr 29;13(1):7017. doi: 10.1038/s41598-023-34024-y.

ABSTRACT

To achieve sustainable development worldwide, the United Nations set 17 Sustainable Development Goals (SDGs) for humanity to reach by 2030. Society is involved in the challenge, with firms playing a crucial role. Thus, a key question is to what extent firms engage with the SDGs. Efforts to map firms’ contributions have mainly focused on analysing companies’ reports based on limited samples and non-real-time data. We present a novel interdisciplinary approach based on analysing big data from an online social network (Twitter) with complex network methods from statistical physics. By doing so, we provide a comprehensive and nearly real-time picture of firms’ engagement with SDGs. Results show that: (1) SDGs themes tie conversations among major UK firms together; (2) the social dimension is predominant; (3) the attention to different SDGs themes varies depending on the community and sector firms belong to; (4) stakeholder engagement is higher on posts related to global challenges compared to general ones; (5) large UK companies and stakeholders generally behave differently from Italian ones. This paper provides theoretical contributions and practical implications relevant to firms, policymakers and management education. Most importantly, it provides a novel tool and a set of keywords to monitor the influence of the private sector on the implementation of the 2030 Agenda.

PMID:37120611 | DOI:10.1038/s41598-023-34024-y

NPJ Digit Med. 2023 Apr 29;6(1):83. doi: 10.1038/s41746-023-00822-x.

ABSTRACT

Data-driven medical care delivery must always respect patient privacy-a requirement that is not easily met. This issue has impeded improvements to healthcare software and has delayed the long-predicted prevalence of artificial intelligence in healthcare. Until now, it has been very difficult to share data between healthcare organizations, resulting in poor statistical models due to unrepresentative patient cohorts. Synthetic data, i.e., artificial but realistic electronic health records, could overcome the drought that is troubling the healthcare sector. Deep neural network architectures, in particular, have shown an incredible ability to learn from complex data sets and generate large amounts of unseen data points with the same statistical properties as the training data. Here, we present a generative neural network model that can create synthetic health records with realistic timelines. These clinical trajectories are generated on a per-patient basis and are represented as linear-sequence graphs of clinical events over time. We use a variational graph autoencoder (VGAE) to generate synthetic samples from real-world electronic health records. Our approach generates health records not seen in the training data. We show that these artificial patient trajectories are realistic and preserve patient privacy and can therefore support the safe sharing of data across organizations.

PMID:37120594 | DOI:10.1038/s41746-023-00822-x

World J Surg Oncol. 2023 Apr 29;21(1):138. doi: 10.1186/s12957-023-03019-4.

ABSTRACT

BACKGROUND: Anaplastic lymphoma kinase (ALK) overexpression and gene alterations have been detected in several mesenchymal tumors, with significant implications for diagnosis, therapy and prognosis. However, few studies have investigated the correlation between ALK expression status and clinicopathological characteristics in patients with gastrointestinal stromal tumors (GISTs).

METHODS: A total of 506 GIST patients were enrolled. Sanger sequencing was employed to detect c-KIT and PDGFRA gene mutations. The tissue microarray (TMA) technique and immunohistochemistry were employed to identify the ALK (clone: 1A4 and D5F3) expression status in the tumor tissues. The ALK gene variants of IHC-positive cases were analyzed by fluorescence in situ hybridization (FISH) and next-generation sequencing (NGS). The clinicopathological data were analyzed using SPSS Statistics 26.0.

RESULTS: Among the 506 GIST patients, the c-KIT mutation accounted for 84.2% (426/506), followed by PDGFRA mutation (10.3%, 52/506), while the wild-type accounted for the least (5.5%, 28/506). ALK-positive expression was detected in PDGFRA-mutant GISTs (7.7%, 4/52) but negative for c-KIT-mutant or wild-type GISTs by IHC. Four ALK IHC-positive patients were all male. The tumors all occurred outside the stomach. The predominant patterns of growth were epithelioid (2/4), spindle (1/4), and mixed type (1/4). They were all identified as high-risk classification according to the National Institutes of Health (NIH) classification. Aberrant ALK mutations were not identified by DNA-based NGS except in one of the 4 cases with amplification by FISH.

CONCLUSION: Our study revealed 7.7% (4/52) of ALK expression in PDGFRA-mutant GISTs, indicating that molecular tests were required to rule out the possibility of PDGFRA-mutant GISTs when encountering ALK-positive mesenchymal tumors with CD117-negative or weakly positive in immunohistochemical staining.

PMID:37120571 | DOI:10.1186/s12957-023-03019-4

Orphanet J Rare Dis. 2023 Apr 29;18(1):98. doi: 10.1186/s13023-023-02697-3.

ABSTRACT

BACKGROUND: This study measured sleep quality among caregivers of patients with Dravet syndrome (DS) and assessed the impacts of mental health problems and caregiver burden on sleep quality.

METHODS: This multicenter, cross-sectional study of patients with DS and their caregivers throughout Germany consisted of a questionnaire and a prospective 4-week diary querying disease characteristics, demographic data, living conditions, nocturnal supervision, and caregivers’ work situations. Sleep quality was assessed using the Pittsburgh Sleeping Quality Index (PSQI). The Hospital Anxiety and Depression Scale (HADS) and the Burden Scale for Family Caregivers (BSFC) were used to measure anxiety, symptoms of depression, and caregiver burden.

RESULTS: Our analysis included 108 questionnaires and 82 four-week diaries. Patients with DS were 49.1% male (n = 53), with a mean age of 13.5 ± 10.0 years. Caregivers were 92.6% (n = 100) female, with a mean age of 44.7 ± 10.6 years. The overall mean PSQI score was 8.7 ± 3.5, with 76.9% of participants (n = 83) scoring 6 or higher, indicating abnormal sleep quality. The HADS for anxiety and depression had overall mean scores of 9.3 ± 4.3 and 7.9 ± 3.7, respectively; 61.8% and 50.9% of participants scored above the cutoff value of 8 for anxiety and depression, respectively. Statistical analyses revealed caregiver anxiety levels and patients’ sleep disturbances as major factors influencing PSQI scores. The overall mean BSFC score of 41.7 ± 11.7 indicates a moderate burden, with 45.3% of caregivers scoring 42 or higher.

CONCLUSIONS: Sleep quality is severely affected among caregivers of patients with DS, correlating with anxiety, comorbidities, and patients’ sleep disturbances. A holistic therapeutic approach should be implemented for patients with DS and their caregivers, focusing on the sleep quality and mental health of caregivers.

TRIAL REGISTRATION: German Clinical Trials Register (DRKS), DRKS00016967. Registered 27 May 2019, http://www.drks.de/DRKS00016967.

PMID:37120555 | DOI:10.1186/s13023-023-02697-3

Clin Epigenetics. 2023 Apr 29;15(1):71. doi: 10.1186/s13148-023-01471-3.

ABSTRACT

BACKGROUND: Clear cell renal cell cancer (ccRCC), the 8th leading cause of cancer-related death in the US, is challenging to treat due to high level intratumoral heterogeneity (ITH) and the paucity of druggable driver mutations. CcRCC is unusual for its high frequency of epigenetic regulator mutations, such as the SETD2 histone H3 lysine 36 trimethylase (H3K36me3), and low frequency of traditional cancer driver mutations. In this work, we examined epigenetic level ITH and defined its relationships with pathologic features, aspects of tumor biology, and SETD2 mutations.

RESULTS: A multi-region sampling approach coupled with EPIC DNA methylation arrays was conducted on a cohort of normal kidney and ccRCC. ITH was assessed using DNA methylation (5mC) and CNV-based entropy and Euclidian distances. We found elevated 5mC heterogeneity and entropy in ccRCC relative to normal kidney. Variable CpGs are highly enriched in enhancer regions. Using intra-class correlation coefficient analysis, we identified CpGs that segregate tumor regions according to clinical phenotypes related to tumor aggressiveness. SETD2 wild-type tumors overall possess greater 5mC and copy number ITH than SETD2 mutant tumor regions, suggesting SETD2 loss contributes to a distinct epigenome. Finally, coupling our regional data with TCGA, we identified a 5mC signature that links regions within a primary tumor with metastatic potential.

CONCLUSION: Taken together, our results reveal marked levels of epigenetic ITH in ccRCC that are linked to clinically relevant tumor phenotypes and could translate into novel epigenetic biomarkers.

PMID:37120552 | DOI:10.1186/s13148-023-01471-3

BMC Health Serv Res. 2023 Apr 29;23(1):413. doi: 10.1186/s12913-023-09441-4.

ABSTRACT

BACKGROUND: International guidelines recommend percutaneous coronary intervention (PCI) to treat acute myocardial infarction (AMI) if PCI can be performed within two hours. PCI is a centralized treatment, and therefore a common trade-off is whether to send AMI patients directly to a hospital that performs PCI, or postpone a potential PCI-treatment by first receiving acute treatment at a local hospital that can not perform PCI. In this paper, we estimate the effect of sending patients directly to a PCI-hospital on AMI mortality.

METHODS: Using nation-wide individual-level data from 2010 to 2015, we studied mortality rates for AMI patients sent directly to a hospital that performs PCI (N=20 336) compared to AMI patients sent to a hospital not performing PCI (N=33 437). Since the underlying health of patients may affect both hospital assignment and mortality, estimates from traditional multivariate risk adjustment models are likely biased. We therefore apply an instrumental variable (IV) model using the historical municipal share sent directly to a PCI-hospital as an instrument for being sent directly to a PCI-hospital.

RESULTS: Patients sent directly to a PCI-hospital are younger and have fewer comorbidities than patients who are first sent to a non-PCI-hospital. IV results suggest that those initially sent to PCI-hospitals have 4.8 percentage points decrease (95% CI (- 18.1)-8.5) in mortality after one month compared to those initially sent to non-PCI-hospitals.

CONCLUSION: Our IV results suggest that there is a non-significant decrease in mortality for AMI patients sent directly to a PCI hospital. The estimates are too imprecise to conclude that health personnel should change their practice and send more patients directly to a PCI-hospital. Moreover, the results may be taken to suggest that health personnel navigate AMI patients to the best treatment option.

PMID:37120541 | DOI:10.1186/s12913-023-09441-4

Biomed Eng Online. 2023 Apr 29;22(1):40. doi: 10.1186/s12938-023-01107-w.

ABSTRACT

BACKGROUND: The progression of Alzheimer’s dementia (AD) can be classified into three stages: cognitive unimpairment (CU), mild cognitive impairment (MCI), and AD. The purpose of this study was to implement a machine learning (ML) framework for AD stage classification using the standard uptake value ratio (SUVR) extracted from ¹⁸F-flortaucipir positron emission tomography (PET) images. We demonstrate the utility of tau SUVR for AD stage classification. We used clinical variables (age, sex, education, mini-mental state examination scores) and SUVR extracted from PET images scanned at baseline. Four types of ML frameworks, such as logistic regression, support vector machine (SVM), extreme gradient boosting, and multilayer perceptron (MLP), were used and explained by Shapley Additive Explanations (SHAP) to classify the AD stage.

RESULTS: Of a total of 199 participants, 74, 69, and 56 patients were in the CU, MCI, and AD groups, respectively; their mean age was 71.5 years, and 106 (53.3%) were men. In the classification between CU and AD, the effect of clinical and tau SUVR was high in all classification tasks and all models had a mean area under the receiver operating characteristic curve (AUC) > 0.96. In the classification between MCI and AD, the independent effect of tau SUVR in SVM had an AUC of 0.88 (p < 0.05), which was the highest compared to other models. In the classification between MCI and CU, the AUC of each classification model was higher with tau SUVR variables than with clinical variables independently, which yielded an AUC of 0.75(p < 0.05) in MLP, which was the highest. As an explanation by SHAP for the classification between MCI and CU, and AD and CU, the amygdala and entorhinal cortex greatly affected the classification results. In the classification between MCI and AD, the para-hippocampal and temporal cortex affected model performance. Especially entorhinal cortex and amygdala showed a higher effect on model performance than all clinical variables in the classification between MCI and CU.

CONCLUSIONS: The independent effect of tau deposition indicates that it is an effective biomarker in classifying CU and MCI into clinical stages using MLP. It is also very effective in classifying AD stages using SVM with clinical information that can be easily obtained at clinical screening.

PMID:37120537 | DOI:10.1186/s12938-023-01107-w

BMC Musculoskelet Disord. 2023 Apr 29;24(1):339. doi: 10.1186/s12891-023-06451-5.

ABSTRACT

BACKGROUND: Lumbosacral canal stenosis is known as the most common cause of back surgery with several complications. Selecting a minimally invasive treatment with high efficacy in such patients is necessary. This study was designed to evaluate the effectiveness of ozone therapy in combination with caudal epidural steroid in patients with lumbar spinal stenosis.

METHODS: A double-blind randomized clinical trial was conducted on 50 patients with lumbar spinal stenosis allocated into two study groups. Under ultrasound guidance, the first group received 80 mg of triamcinolone hexavalent with 4 mL of Marcaine 0.5% and 6 mL of distilled water to the caudal epidural space. The second group received an injection similar to the first group, combined with 10 mL of ozone (O2-O3) gas at a concentration of 10 µg/cc. The patients were followed at baseline, one, and six months after injection with clinical outcomes measures using Visual Analog Scale (VAS), Walking Distance (WD) and Oswestry Disability Index (ODI).

RESULTS: The mean age of subjects, 30 males (60%) and 20 females (40%), was reported as 64.51 ± 7.19 years old. Reduction of pain intensity based on VAS score was statistically significant in both groups at follow-up periods (P < 0.001). The VAS changes in the first month and sixth months showed no significant difference between the two groups (P = 0.28 and P = 0.33, respectively). The improvement in disability index (ODI) in both types of treatment during follow-up was significant (P < 0.0001), and there was no difference between the two treatment groups in one month and six months (P = 0.48 and P = 0.88, respectively). As for walking distance, the improvement process with both types of treatment during follow-up periods was significant (P < 0.001). However, after one and six months of treatment, the rate of improvement in patients’ walking distance in the caudal epidural steroid injection plus ozone group was significantly higher than in the epidural steroid group (p = 0.026 and p = 0.017, respectively).

CONCLUSIONS: In this study, the results of VAS and ODI outcomes showed that caudal epidural steroid injection combined with ozone has no advantage over caudal epidural steroid injection alone. Interestingly, our results demonstrated that the group receiving caudal epidural steroid injection plus ozone scored significantly higher on the walking distance index than the group receiving caudal epidural steroid alone.

TRIAL REGISTRATION: IRCT IRCT20090704002117N2 (registration date: 07/08/2019).

PMID:37120532 | DOI:10.1186/s12891-023-06451-5

BMC Plant Biol. 2023 Apr 29;23(1):228. doi: 10.1186/s12870-023-04215-w.

ABSTRACT

BACKGROUND: Moth bean (Vigna aconitifolia) is an underutilized, protein-rich legume that is grown in arid and semi-arid areas of south Asia and is highly resistant to abiotic stresses such as heat and drought. Despite its economic importance, the crop remains unexplored at the genomic level for genetic diversity and trait mapping studies. To date, there is no report of SNP marker discovery and association mapping of any trait in this crop. Therefore, this study aimed to dissect the genetic diversity, population structure and marker-trait association for the flowering trait in a diversity panel of 428 moth bean accessions using genotyping by sequencing (GBS) approach.

RESULTS: A total of 9078 high-quality single nucleotide polymorphisms (SNPs) were discovered by genotyping of 428 moth bean accessions. Model-based structure analysis and PCA grouped the moth bean accessions into two subpopulations. Cluster analysis revealed accessions belonging to the Northwestern region of India had higher variability than accessions from the other regions suggesting that this region represents its center of diversity. AMOVA revealed more variations within individuals (74%) and among the individuals (24%) than among the populations (2%). Marker-trait association analysis using seven multi-locus models including mrMLM, FASTmrEMMA FASTmrEMMA, ISIS EM-BLASSO, MLMM, BLINK and FarmCPU revealed 29 potential genomic regions for the trait days to 50% flowering, which were consistently detected in three or more models. Analysis of the allelic effect of the major genomic regions explaining phenotypic variance of more than 10% and those detected in at least 2 environments showed 4 genomic regions with significant phenotypic effect on this trait. Further, we also analyzed genetic relationships among the Vigna species using SNP markers. The genomic localization of moth bean SNPs on genomes of closely related Vigna species demonstrated that maximum numbers of SNPs were getting localized on Vigna mungo. This suggested that the moth bean is most closely related to V. mungo.

CONCLUSION: Our study shows that the north-western regions of India represent the center of diversity of the moth bean. Further, the study revealed flowering-related genomic regions/candidate genes which can be potentially exploited in breeding programs to develop early-maturity moth bean varieties.

PMID:37120525 | DOI:10.1186/s12870-023-04215-w

J Neuroeng Rehabil. 2023 Apr 29;20(1):55. doi: 10.1186/s12984-023-01178-9.

ABSTRACT

BACKGROUND: An incomplete spinal cord injury (SCI) refers to remaining sensorimotor function below the injury with the possibility for the patient to regain walking abilities. However, these patients often suffer from diverse gait deficits, which are not objectively assessed in the current clinical routine. Wearable inertial sensors are a promising tool to capture gait patterns objectively and started to gain ground for other neurological disorders such as stroke, multiple sclerosis, and Parkinson’s disease. In this work, we present a data-driven approach to assess walking for SCI patients based on sensor-derived outcome measures. We aimed to (i) characterize their walking pattern in more depth by identifying groups with similar walking characteristics and (ii) use sensor-derived gait parameters as predictors for future walking capacity.

METHODS: The dataset analyzed consisted of 66 SCI patients and 20 healthy controls performing a standardized gait test, namely the 6-min walking test (6MWT), while wearing a sparse sensor setup of one sensor attached to each ankle. A data-driven approach has been followed using statistical methods and machine learning models to identify relevant and non-redundant gait parameters.

RESULTS: Clustering resulted in 4 groups of patients that were compared to each other and to the healthy controls. The clusters did differ in terms of their average walking speed but also in terms of more qualitative gait parameters such as variability or parameters indicating compensatory movements. Further, using longitudinal data from a subset of patients that performed the 6MWT several times during their rehabilitation, a prediction model has been trained to estimate whether the patient’s walking speed will improve significantly in the future. Including sensor-derived gait parameters as inputs for the prediction model resulted in an accuracy of 80%, which is a considerable improvement of 10% compared to using only the days since injury, the present 6MWT distance, and the days until the next 6MWT as predictors.

CONCLUSIONS: In summary, the work presented proves that sensor-derived gait parameters provide additional information on walking characteristics and thus are beneficial to complement clinical walking assessments of SCI patients. This work is a step towards a more deficit-oriented therapy and paves the way for better rehabilitation outcome predictions.

PMID:37120519 | DOI:10.1186/s12984-023-01178-9