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Eyal Klang,1– 3,&ast; Shelly Soffer,3– 5,&ast; Yiftach Barash,1– 3 Eyal Shachar,2,6 Adi Lahat2,6 1Department of Diagnostic Imaging, Sheba Medical Center, Ramat Gan, Israel; 2Sackler Medical School, Tel Aviv University, Tel Aviv, Israel; 3DeepVision Lab, Sheba Medical Center, Ramat Gan, Israel; 4Internal Medicine B, Samson Assuta Ashdod University Hospital, Ashdod, Israel; 5Ben-Gurion University of the Negev, Be’er Sheva, Israel; 6Department of Gastroenterology, Sheba Medical Center, Ramat Gan, Israel&ast;These authors contributed equally to this workCorrespondence: Shelly Soffer, Internal Medicine B, Samson Assuta Ashdod University Hospital, Ha-Refu’a St 7, Ashdod, 7747629, Israel, Tel +973-8-300-4100, Fax +972-3-5357315, Email soffer.shelly@gmail.comBackground: Since the discovery of Helicobacter pylori (H. pylori) 40 years ago, treatment options have changed dramatically adjacent to new drugs development and the emergence of multi-drug resistance. In this study, we aimed to assess published literature on treatment regimens for H. pylori in the past four decades, with regards to specific drug resistance and geographic distribution.Methods: We have queried PubMed for all available H. pylori-related entries published during 1987– 2020. For each entry, we retrieved the title, abstract, and keywords. Two gastrointestinal specialists decided in consensus on a list of terms to classify entries. The terms specify types of treatment and different types of regimens. Annual trends of publications were plotted for different treatment types and sub-analyses.Results: Overall, 47,170 H. pylori-related entries were published between 1987 and 2020. Medication treatment showed the highest number of total publications 6183/47,170 (13.1%), followed by antibiotics 5493/47,170 (11.6%), and probiotic which was substantially lower (0.7%). For treatment regimens type, triple therapy had the highest number of publications 2400/47,170 (5.1%) followed by sequential therapy 342/47,170 (0.7%), and bismuth quadruple therapy 115/47,170 (0.2%). Analysis by antibiotic resistance showed that clarithromycin treatment had the highest number of publications 3537/47,170 (7.5%). Since 2013, there is a steep upward slope (5.1± 0.7 publications/year, p < 0.001) for publications originating from China.Conclusion: Our findings reflect the changes of the commonly prescribed regimens along the years, following the occurrence and the rise of H. pylori antibiotic resistance. In recent years, there is a steep rise in publications in countries with policies of screening and eradication for gastric cancer prevention, which is currently not applied in Western countries. A text-mining analysis of H. pylori publications contributes to the understanding of treatment options and development trends worldwide.Keywords: Helicobacter pylori, antibiotics, treatment regimens, antibiotic resistance

Eyal Klang,1– 3 Yiftach Barash,1,3 Asaf Levartovsky,2,4 Noam Barkin Lederer,2,4 Adi Lahat2,4 1Department of Diagnostic Imaging, Chaim Sheba Medical Center, Tel Hashomer, Israel; 2Sackler Medical School, Tel Aviv University, Tel Aviv, Israel; 3DeepVision Lab (3), Chaim Sheba Medical Center, Tel Hashomer, Israel; 4Department of Gastroenterology, Chaim Sheba Medical Center, Tel Hashomer, IsraelCorrespondence: Adi LahatDepartment of Gastroenterology, Sheba Medical Center, Tel Hashomer, Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, IsraelEmail zokadi@gmail.comBackground and Aim: Endoscopic differentiation between malignant and benign gastric ulcers (GU) affects further evaluation and prognosis. The aim of our study was to evaluate a deep learning algorithm for discrimination between benign and malignant GU in a database of endoscopic ulcer images.Methods: We retrospectively collected consecutive upper gastrointestinal endoscopy images of GU performed between 2011 and 2019 at the Sheba Medical Center. All ulcers had a corresponding histopathology result of either benign peptic ulcer or gastric adenocarcinoma. A convolutional neural network (CNN) was trained to classify the images into either benign or malignant. Endoscopies from 2011 to 2017 were used for training (2011– 2015) and validation (2016– 2017). Hyper-parameters, image augmentation and pre-training on Google images obtained images were evaluated on the validation data. Held-out data from 2018 to 2019 was used for testing the final model.Results: Overall, the Sheba dataset included 1978 GU images; 1894 images from benign GU and 84 images of malignant ulcers. The final CNN model showed an AUC 0.91 (95% CI 0.85– 0.96) for detecting malignant ulcers. For cut-off probability 0.5, the network showed a sensitivity of 92% and specificity of 75% for malignant ulcers.Conclusion: Our study displays the applicability of a CNN model for automated evaluation of gastric ulcers images for malignant potential. Following further research, the algorithm may improve accuracy of differentiating benign from malignant ulcers during endoscopies and assist in patients’ stratification, allowing accelerated patients management and individualized approach towards surveillance endoscopy.Keywords: gastric ulcer, malignant, benign, artificial intelligence, AI, deep learning

To increase the competitiveness of railway transport the authors propose to develop of railway tourism in Ukraine. The article aims to systematize the advantages and disadvantages of railway and automobile tourism and develop efficient areas of their use in this context. Moreover, the authors set a goal to develop a methodical approach to determine the costs of the company that will organize transportations on the specific tourist route. Studies have shown that tourist traffic by railway transport has high potential, provided that it will use modern comfortable rolling stock and have in its structure not only the cars for tourist transportation, but also dining car, casino car, disco car (during the day game car for kids) and others. The above mentioned set of cars should be recommended for cruise travel with a period from 8 to 28 days, for shorter journeys the tourist train may not include casino car, disco car (during the day game car for kids) and others. The final choice of tourist traffic should be justified by economic calculations using the method proposed by this article. The approach is based on accounting all complex of costs associated with the technological process of tourism services. It includes the costs for the process of transportation itself, delivery of additional services of infrastructure and rolling stock owners, pay for transfer transportations, services of hotels, restaurants, cafes, travel agencies, advertising organizations and own costs of travel company. It makes possible to determine the costs of travel package more accurately, taking into account the demand for this type of tourist traffic. As a result of the proposed measures one can expect increase of passenger traffic on the railroad, image improvement of the Ukrainian railways within the country and abroad, attraction of additional funds to renew the outdated infrastructure and rolling stock, restoration of the objects of cultural and historical significance, creation of new jobs, and restraining of migration processes in western Ukraine. [ABSTRACT FROM AUTHOR]

Superconducting loops exhibit macroscopic quantum phenomena that have far-reaching implications; magnetic flux periodicity and flux quantization are the key to our understanding of fundamental properties of superconductors and are the basis for many applications. In superconducting rings, the electrical current responds to a magnetic flux by having a periodicity of h/2e, where the ratio of Planck’s constant and the elementary charge defines the magnetic flux quantum h/e. The well-known h/2e periodicity is a hallmark for electronic pairing in superconductors and is considered evidence for the existence of Cooper pairs. Here, we show that in contrast to this long-held belief, rings of many superconductors bear an h/e periodicity. These superconductors include the high-temperature superconductors, Sr2RuO4, the heavy-fermion superconductors, as well as all other unconventional superconductors with nodes (zeros) in the energy gap, and conventional s-wave superconductors with small gaps. As we show, the 50-year-old Bardeen–Cooper–Schrieffer theory of superconductivity implies that for loops of such superconductors the ground-state energies and consequently also the supercurrents are generically h/e periodic. [ABSTRACT FROM AUTHOR]

Mapping transcriptomic variations using either short- or long-read RNA sequencing is a staple of genomic research. Long reads are able to capture entire isoforms and overcome repetitive regions, whereas short reads still provide improved coverage and error rates. Yet, open questions remain, such as how to quantitatively compare the technologies, can we combine them, and what is the benefit of such a combined view? We tackle these questions by first creating a pipeline to assess matched long- and short-read data using a variety of transcriptome statistics. We find that across data sets, algorithms, and technologies, matched short-read data detects ∼30% more splice junctions, such that ∼10%-30% of the splice junctions included at ≥20% by short reads are missed by long reads. In contrast, long reads detect many more intron-retention events and can detect full isoforms, pointing to the benefit of combining the technologies. We introduce MAJIQ-L, an extension of the MAJIQ software, to enable a unified view of transcriptome variations from both technologies and demonstrate its benefits. Our software can be used to assess any future long-read technology or algorithm and can be combined with short-read data for improved transcriptome analysis., (© 2024 Han et al.; Published by Cold Spring Harbor Laboratory Press.)

Purpose: While mammography is considered the gold standard for screening women for breast cancer, its accuracy declines in women with dense breasts. The purpose of the study is to evaluate the diagnostic accuracy of contrast enhanced mammography (CEM) for detecting breast cancer in intermediate and high-risk women, including those with genetic predispositions, over a decade-long cohort at a tertiary center., Methods: We retrospectively analyzed all CEM examinations performed for screening purposes at a tertiary center between 2012 and 2023. Data were extracted from imaging reports and from medical records. All biopsies performed up to one year following CEM examinations, and all breast cancer cases were extracted. BI-RADS scores from CEM reports were compared to biopsy results and to imaging follow-up. Sensitivity, specificity, positive and negative predictive values, area under the ROC curve (AUC), and cumulative cancer detection rate were calculated., Results: Overall 5,424 screening CEM examinations were analyzed. The mean age was 54.8 ± 8.9 years. Family history of breast cancer was recorded in 1,134/5,424 (20.9 %) women. Most women (4,606/5,424, 84.9 %) had dense breasts (BI-RADS C-D). Overall, 628 biopsies were performed within one year of screening, and 74 cancers were detected. CEM had sensitivity 95.9 % (71/74), specificity 81.8 % (4378/5350), positive predictive value 6.8 % (71/1043), negative predictive value 99.9 % (4378/4381). The cancer detection rate for CEM was 13.1 per 1,000 cases, and the AUC was 0.923., Conclusions: CEM is a viable breast cancer screening method for women with dense breasts. Future prospective studies are needed to evaluate the long-term prognostic impact of CEM screening., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Relapsed or refractory B-cell acute lymphoblastic leukemia (B-ALL) is a major cause of pediatric cancer-related deaths. Relapse-specific mutations do not account for all chemotherapy failures in B-ALL patients, suggesting additional mechanisms of resistance. By mining RNA sequencing datasets of paired diagnostic/relapse pediatric B-ALL samples, we discovered pervasive alternative splicing (AS) patterns linked to relapse and affecting drivers of resistance to glucocorticoids, antifolates, and thiopurines. Most splicing variations represented cassette exon skipping, "poison" exon inclusion, and intron retention, phenocopying well-documented loss-of-function mutations. In contrast, relapse-associated AS of NT5C2 mRNA yielded an isoform with the functionally uncharacterized in-frame exon 6a. Incorporation of the 8-amino acid sequence SQVAVQKR into this enzyme created a putative phosphorylation site and resulted in elevated nucleosidase activity, which is a known consequence of gain-of-function mutations in NT5C2 and a common determinant of 6-mercaptopurine resistance. Consistent with this finding, NT5C2ex6a and the R238W hotspot variant conferred comparable levels of resistance to 6-mercaptopurine in B-ALL cells both in vitro and in vivo. Furthermore, both NT5C2ex6a and the R238W variant induced collateral sensitivity to the inosine monophosphate dehydrogenase inhibitor mizoribine. These results ascribe to splicing perturbations an important role in chemotherapy resistance in relapsed B-ALL and suggest that inosine monophosphate dehydrogenase inhibitors, including the commonly used immunosuppressive agent mycophenolate mofetil, could be a valuable therapeutic option for treating thiopurine-resistant leukemias. Significance: Alternative splicing is a potent mechanism of acquired drug resistance in relapsed/refractory acute lymphoblastic leukemias that has diagnostic and therapeutic implications for patients who lack mutations in known chemoresistance genes., (©2024 The Authors; Published by the American Association for Cancer Research.)

Previously, we showed that the germ cell-specific nuclear protein RBMXL2 represses cryptic splicing patterns during meiosis and is required for male fertility (Ehrmann et al., 2019). Here, we show that in somatic cells the similar yet ubiquitously expressed RBMX protein has similar functions. RBMX regulates a distinct class of exons that exceed the median human exon size. RBMX protein-RNA interactions are enriched within ultra-long exons, particularly within genes involved in genome stability, and repress the selection of cryptic splice sites that would compromise gene function. The RBMX gene is silenced during male meiosis due to sex chromosome inactivation. To test whether RBMXL2 might replace the function of RBMX during meiosis we induced expression of RBMXL2 and the more distantly related RBMY protein in somatic cells, finding each could rescue aberrant patterns of RNA processing caused by RBMX depletion. The C-terminal disordered domain of RBMXL2 is sufficient to rescue proper splicing control after RBMX depletion. Our data indicate that RBMX and RBMXL2 have parallel roles in somatic tissues and the germline that must have been conserved for at least 200 million years of mammalian evolution. We propose RBMX family proteins are particularly important for the splicing inclusion of some ultra-long exons with increased intrinsic susceptibility to cryptic splice site selection., Competing Interests: CS, SL, SA, GH, CD, KC, MG, IY, KJ, MK, IE, GS, SC, JM, SW, YB, DE No competing interests declared, (© 2023, Siachisumo, Luzzi, Aldalaqan et al.)

Background: Differential diagnosis in radiology relies on the accurate identification of imaging patterns. The use of large language models (LLMs) in radiology holds promise, with many potential applications that may enhance the efficiency of radiologists' workflow. The study aimed to evaluate the efficacy of generative pre-trained transformer (GPT)-4, a LLM, in providing differential diagnoses in neuroradiology, comparing its performance with board-certified neuroradiologists., Methods: Sixty neuroradiology reports with variable diagnoses were inserted into GPT-4, which was tasked with generating a top-3 differential diagnosis for each case. The results were compared to the true diagnoses and to the differential diagnoses provided by three blinded neuroradiologists. Diagnostic accuracy and agreement between readers were assessed., Results: Of the 60 patients (mean age 47.8 years, 65% female), GPT-4 correctly included the diagnoses in its differentials in 61.7% (37/60) of cases, while the neuroradiologists' accuracy ranged from 63.3% (38/60) to 73.3% (44/60). Agreement between GPT-4 and the neuroradiologists, and among the neuroradiologists was fair to moderate [Cohen's kappa (kw) 0.34-0.44 and kw 0.39-0.54, respectively]., Conclusions: GPT-4 shows potential as a support tool for differential diagnosis in neuroradiology, though it was outperformed by human experts. Radiologists should remain mindful to the limitations of LLMs, while harboring their potential to enhance educational and clinical work., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-24-200/coif). The authors have no conflicts of interest to declare., (2024 AME Publishing Company. All rights reserved.)

RNA-sequencing (RNA-seq) is widely adopted for transcriptome analysis but has inherent biases which hinder the comprehensive detection and quantification of alternative splicing. To address this, we present an efficient targeted RNA-seq method that greatly enriches for splicing-informative junction-spanning reads. Local Splicing Variation sequencing (LSV-seq) utilizes multiplexed reverse transcription from highly scalable pools of primers anchored near splicing events of interest. Primers are designed using Optimal Prime, a novel machine learning algorithm trained on the performance of thousands of primer sequences. In experimental benchmarks, LSV-seq achieves high on-target capture rates and concordance with RNA-seq, while requiring significantly lower sequencing depth. Leveraging deep learning splicing code predictions, we used LSV-seq to target events with low coverage in GTEx RNA-seq data and newly discover hundreds of tissue-specific splicing events. Our results demonstrate the ability of LSV-seq to quantify splicing of events of interest at high-throughput and with exceptional sensitivity.

Genome-wide association studies (GWAS) have identified thousands of putative disease causing variants with unknown regulatory effects. Efforts to connect these variants with splicing quantitative trait loci (sQTLs) have provided functional insights, yet sQTLs reported by existing methods cannot explain many GWAS signals. We show current sQTL modeling approaches can be improved by considering alternative splicing representation, model calibration, and covariate integration. We then introduce MAJIQTL, a new pipeline for sQTL discovery. MAJIQTL includes two new statistical methods: a weighted multiple testing approach for sGene discovery and a model for sQTL effect size inference to improve variant prioritization. By applying MAJIQTL to GTEx, we find significantly more sGenes harboring sQTLs with functional significance. Notably, our analysis implicates the novel variant rs582283 in Alzheimer's disease. Using antisense oligonucleotides, we validate this variant's effect by blocking the implicated YBX3 binding site, leading to exon skipping in the gene MS4A3., Competing Interests: Competing Interests: The authors declare that they have no competing interests.

Background and Objective: Acute myelogenous leukemia (AML) is a common blood cancer marked by heterogeneity in disease and diverse genetic abnormalities. Additional therapies are needed as the 5-year survival remains below 30%. Trametinib is a mitogen-activated extracellular signal-regulated kinase (MEK) inhibitor that is widely used in solid tumors and also in tumors with activating RAS mutations. A subset of patients with AML carry activating RAS mutations; however, a small-scale clinical trial with trametinib showed little efficacy. Here, we sought to identify transcriptomic determinants of trametinib sensitivity in AML., Methods: We tested the activity of trametinib against a panel of tumor cells from patients with AML ex vivo and compared this with RNA sequencing (RNA-Seq) data from untreated blasts from the same patient samples. We then used a correlation analysis between gene expression and trametinib sensitivity to identify potential biomarkers predictive of drug response., Results: We found that a subset of AML tumor cells were sensitive to trametinib ex vivo, only a fraction of which (3/10) carried RAS mutations. On the basis of our RNA-Seq analysis we found that markers of trametinib sensitivity are associated with a myeloid differentiation profile that includes high expression of CD14 and CLEC7A (Dectin-1), similar to the gene expression profile of monocytes. Further characterization confirmed that trametinib-sensitive samples display features of monocytic differentiation with high CD14 surface expression and were enriched for the M4 subtypes of the FAB classification., Conclusions: Our study identifies additional molecular markers that can be used with molecular features including RAS status to identify patients with AML that may benefit from trametinib treatment., (© 2024. The Author(s).)

Purpose: In BRCA germline pathogenic sequence variants (PSV) carriers aged 30-39 years imaging is recommended at six-month intervals. The European society for medical oncology recommendation of the use of 6-monthly MRI six-monthly MRI screening is being considered at our institution, particularly for younger carriers under the age of 35, although it is not mandatory. If 6-monthly MRI is unavailable, annual MRI may be supplemented by ultrasound (with or without mammography). The aim of this study was to evaluate the utility of ultrasound screening added to mammography, as a 6-month supplement to annual MRI in BRCA PSV carriers aged 30-39 years., Materials and Methods: This IRB approved retrospective study included BRCA PSV carriers aged 30-39 years, who underwent breast cancer screening at our institution between January 2015 and March 2023. Participants were divided into two groups, those who had supplemental whole-breast US and mammography at six months and underwent screening before March 2019, and those who had only mammography without supplemental US and enrolled in screening after March 2019. Patient characteristics, cancer detection rates and cancer characteristics were compared between the two groups., Results: Overall, 200 asymptomatic BRCA1/2 PSV carriers undergoing screening in our institution were included in the study. Mean age was 35.7 ± 3.5 years, and mean follow-up time was 37.4 ± 38.0 months. There were 118 (59 %) women screened with supplemental US, and 82 (41 %) women without. Eight cancers were diagnosed during the study period, four in women with supplemental US and four in women without. The sensitivity of whole-breast screening US was 25 % (1/4), specificity 85.7 % (222/259), PPV 2.6 % (1/38), and NPV 98.7 % (222/225). Of the four cancers detected in women screened with supplemental US, one was diagnosed by whole-breast US, two by MRI, and one by mammography. Of eight cancers included in this study, two were not detectable by targeted second-look US. All eight cancers were detectable by MRI., Conclusion: The addition of whole-breast ultrasound to mammography and MRI screening in BRCA PSV carriers aged 30-39 years offered limited incremental benefit. MRI with 6 months supplemental mammography without US detected all cancer cases., Competing Interests: Declaration of competing interest All authors declare none., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Objectives: This study aims to assess the performance of a multimodal artificial intelligence (AI) model capable of analyzing both images and textual data (GPT-4V), in interpreting radiological images. It focuses on a range of modalities, anatomical regions, and pathologies to explore the potential of zero-shot generative AI in enhancing diagnostic processes in radiology., Methods: We analyzed 230 anonymized emergency room diagnostic images, consecutively collected over 1 week, using GPT-4V. Modalities included ultrasound (US), computerized tomography (CT), and X-ray images. The interpretations provided by GPT-4V were then compared with those of senior radiologists. This comparison aimed to evaluate the accuracy of GPT-4V in recognizing the imaging modality, anatomical region, and pathology present in the images., Results: GPT-4V identified the imaging modality correctly in 100% of cases (221/221), the anatomical region in 87.1% (189/217), and the pathology in 35.2% (76/216). However, the model's performance varied significantly across different modalities, with anatomical region identification accuracy ranging from 60.9% (39/64) in US images to 97% (98/101) and 100% (52/52) in CT and X-ray images (p < 0.001). Similarly, pathology identification ranged from 9.1% (6/66) in US images to 36.4% (36/99) in CT and 66.7% (34/51) in X-ray images (p < 0.001). These variations indicate inconsistencies in GPT-4V's ability to interpret radiological images accurately., Conclusion: While the integration of AI in radiology, exemplified by multimodal GPT-4, offers promising avenues for diagnostic enhancement, the current capabilities of GPT-4V are not yet reliable for interpreting radiological images. This study underscores the necessity for ongoing development to achieve dependable performance in radiology diagnostics., Clinical Relevance Statement: Although GPT-4V shows promise in radiological image interpretation, its high diagnostic hallucination rate (> 40%) indicates it cannot be trusted for clinical use as a standalone tool. Improvements are necessary to enhance its reliability and ensure patient safety., Key Points: GPT-4V's capability in analyzing images offers new clinical possibilities in radiology. GPT-4V excels in identifying imaging modalities but demonstrates inconsistent anatomy and pathology detection. Ongoing AI advancements are necessary to enhance diagnostic reliability in radiological applications., (© 2024. The Author(s).)

Large language models (LLMs) are transforming the field of natural language processing (NLP). These models offer opportunities for radiologists to make a meaningful impact in their field. NLP is a part of artificial intelligence (AI) that uses computer algorithms to study and understand text data. Recent advances in NLP include the Attention mechanism and the Transformer architecture. Transformer-based LLMs, such as GPT-4 and Gemini, are trained on massive amounts of data and generate human-like text. They are ideal for analysing large text data in academic research and clinical practice in radiology. Despite their promise, LLMs have limitations, including their dependency on the diversity and quality of their training data and the potential for false outputs. Albeit these limitations, the use of LLMs in radiology holds promise and is gaining momentum. By embracing the potential of LLMs, radiologists can gain valuable insights and improve the efficiency of their work. This can ultimately lead to improved patient care., Competing Interests: None declared., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology.)

Background: Traumatic knee injuries are challenging to diagnose accurately through radiography and to a lesser extent, through CT, with fractures sometimes overlooked. Ancillary signs like joint effusion or lipo-hemarthrosis are indicative of fractures, suggesting the need for further imaging. Artificial Intelligence (AI) can automate image analysis, improving diagnostic accuracy and help prioritizing clinically important X-ray or CT studies., Objective: To develop and evaluate an AI algorithm for detecting effusion of any kind in knee X-rays and selected CT images and distinguishing between simple effusion and lipo-hemarthrosis indicative of intra-articular fractures., Methods: This retrospective study analyzed post traumatic knee imaging from January 2016 to February 2023, categorizing images into lipo-hemarthrosis, simple effusion, or normal. It utilized the FishNet-150 algorithm for image classification, with class activation maps highlighting decision-influential regions. The AI's diagnostic accuracy was validated against a gold standard, based on the evaluations made by a radiologist with at least four years of experience., Results: Analysis included CT images from 515 patients and X-rays from 637 post traumatic patients, identifying lipo-hemarthrosis, simple effusion, and normal findings. The AI showed an AUC of 0.81 for detecting any effusion, 0.78 for simple effusion, and 0.83 for lipo-hemarthrosis in X-rays; and 0.89, 0.89, and 0.91, respectively, in CTs., Conclusion: The AI algorithm effectively detects knee effusion and differentiates between simple effusion and lipo-hemarthrosis in post-traumatic patients for both X-rays and selected CT images further studies are needed to validate these results., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)