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Clear-cell renal cell carcinoma (ccRCC) is a common therapy resistant disease with aberrant angiogenic and immunosuppressive features. Patients with metastatic disease are treated with targeted therapies based on clinical features: low-risk patients are usually treated with anti-angiogenic drugs and intermediate/high-risk patients with immune therapy. However, there are no biomarkers available to guide treatment choice for these patients. A recently published phase II clinical trial observed a correlation between ccRCC patients' clustering and their response to targeted therapy. However, the clustering of these groups was not distinct. Here, we analyzed the gene expression profile of 469 ccRCC patients, using featured selection technique, and have developed a refined 66-gene signature for improved sub-classification of patients. Moreover, we have identified a novel comprehensive expression profile to distinguish between migratory stromal and immune cells. Furthermore, the proposed 66-gene signature was validated using a different cohort of 64 ccRCC patients. These findings are foundational for the development of reliable biomarkers that may guide treatment decision-making and improve therapy response in ccRCC patients.

Due to the increasing global population and the growing demand for food worldwide as well as changes in weather conditions and the availability of water, artificial intelligence (AI) such as expert systems, natural language processing, speech recognition, and machine vision have changed not only the quantity but also the quality of work in the agricultural sector. Researchers and scientists are now moving toward the utilization of new IoT technologies in smart farming to help farmers use AI technology in the development of improved seeds, crop protection, and fertilizers. This will improve farmers’ profitability and the overall economy of the country. AI is emerging in three major categories in agriculture, namely soil and crop monitoring, predictive analytics, and agricultural robotics. In this regard, farmers are increasingly adopting the use of sensors and soil sampling to gather data to be used by farm management systems for further investigations and analyses. This article contributes to the field by surveying AI applications in the agricultural sector. It starts with background information on AI, including a discussion of all AI methods utilized in the agricultural industry, such as machine learning, the IoT, expert systems, image processing, and computer vision. A comprehensive literature review is then provided, addressing how researchers have utilized AI applications effectively in data collection using sensors, smart robots, and monitoring systems for crops and irrigation leakage. It is also shown that while utilizing AI applications, quality, productivity, and sustainability are maintained. Finally, we explore the benefits and challenges of AI applications together with a comparison and discussion of several AI methodologies applied in smart farming, such as machine learning, expert systems, and image processing.

Abstract In this paper, an in silico proof of concept of a spinristor is proposed and provided; a new electronic component that combines a spin‐filter and a memristor in a single molecule, useful for in‐memory processing. It builds on the idea of an open‐shell transition metal ion enclosed within an elliptical fullerene connected to a pair of electrodes. The spin‐ and electronic‐polarization induced by the enclosed open‐shell metallic ion leads to differential rectification of the electrons at low voltages applied between the source–drain electrodes, VSD. The position of the encapsulated ion can be switched by a high VSD which leads to a change in the direction of the rectification and the spin‐filtering ratio. The system can thus be used as a switching rectifier, that is, a memristor and a spin‐filter; therefore, a spinristor. The effect of different linkers on the function of the proposed device is further analyzed to show that linkers reduce the overall conductivity by an order of magnitude, but improve the spin‐filtering ratio. The computations suggest that nitrile and isocyanide linkers enhance the rectification, too. To the best of the authors’ knowledge, spinristor has no macroscopic counterpart in electronics, so far.

IntroductionA novel classification scheme for endplate lesions, based on T2-weighted images from magnetic resonance imaging (MRI) scan, has been recently introduced and validated. The scheme categorizes intervertebral spaces as “normal,” “wavy/irregular,” “notched,” and “Schmorl's node.” These lesions have been associated with spinal pathologies, including disc degeneration and low back pain. The exploitation of an automatic tool for the detection of the lesions would facilitate clinical practice by reducing the workload and the diagnosis time. The present work exploits a deep learning application based on convolutional neural networks to automatically classify the type of lesion.MethodsT2-weighted MRI scans of the sagittal lumbosacral spine of consecutive patients were retrospectively collected. The middle slice of each scan was manually processed to identify the intervertebral spaces from L1L2 to L5S1, and the corresponding lesion type was labeled. A total of 1,559 gradable discs were obtained, with the following types of distribution: “normal” (567 discs), “wavy/irregular” (485), “notched” (362), and “Schmorl's node” (145). The dataset was divided randomly into a training set and a validation set while preserving the original distribution of lesion types in each set. A pretrained network for image classification was utilized, and fine-tuning was performed using the training set. The retrained net was then applied to the validation set to evaluate the overall accuracy and accuracy for each specific lesion type.ResultsThe overall rate of accuracy was found equal to 88%. The accuracy for the specific lesion type was found as follows: 91% (normal), 82% (wavy/irregular), 93% (notched), and 83% (Schmorl's node).DiscussionThe results indicate that the deep learning approach achieved high accuracy for both overall classification and individual lesion types. In clinical applications, this implementation could be employed as part of an automatic detection tool for pathological conditions characterized by the presence of endplate lesions, such as spinal osteochondrosis.

Breast-conserving surgery (BCS) with negative resection margins decreases the locoregional recurrence rate. Breast cancer size is one of the main determinants of Tumor-Node-Metastasis (TNM) staging. Our study aimed to investigate the accuracy of supine 3D automated breast ultrasound (3D ABUS) compared to prone 3D ABUS in the evaluation of tumor size in breast cancer patient candidates for BCS. In this prospective two-center study (Groups 1 and 2), we enrolled patients with percutaneous biopsy-proven early-stage breast cancer, in the period between June 2019 and May 2020. Patients underwent hand-held ultrasound (HHUS), contrast-enhanced magnetic resonance imaging (CE-MRI) and 3D ABUS—supine 3D ABUS in Group 1 and prone 3D ABUS in Group 2. Histopathological examination (HE) was considered the reference standard. Bland–Altman analysis and plots were used. Eighty-eight patients were enrolled. Compared to prone, supine 3D ABUS showed better agreement with HE, with a slight tendency toward underestimation (mean difference of −2 mm). Supine 3D ABUS appears to be a useful tool and more accurate than HHUS in the staging of breast cancer.

Background: Commercial insurance data show that chronic opioid use in opioid-naive patients occurs in 1.5% to 8% of patients undergoing surgical procedures, but little is known about patients with Medicaid. Methods: Opioid prescription data and medical coding data from 4,788 Medicaid patients who underwent cholecystectomy were analyzed to determine opioid use patterns. Results: A total of 54.4% of patients received opioids prior to surgery, and 38.8% continued to fill opioid prescriptions chronically; 27.1% of opioid-naive patients continued to get opioids chronically. Patients who received ≥50 MME/d had nearly 8 times the odds of chronic opioid use. Each additional opioid prescription filled within 30 days was associated with increased odds of chronic use (odds ratio: 1.71). Conclusion: Opioid prescriptions are common prior to cholecystectomy in Medicaid patients, and 38.8% of patients continue to receive opioid prescriptions well after surgical recovery. Even 27.1% of opioid-naive patients continued to receive opioid prescriptions chronically.

Abstract Recent discovery of an unusual bond between Na and B in NaBH3 − motivated us to look for potentially similar bonds, which remained unnoticed among systems isoelectronic with NaBH3 −. Here, we report a novel family of collective interactions and a measure called exchange-correlation interaction collectivity index (ICIXC; $${ICI}\in \left[{{{{\mathrm{0,1}}}}}\right]$$ I C I ∈ 0, 1 ) to characterize the extent of collective versus pairwise bonding. Unlike conventional bonds in which ICIXC remains close to one, in collective interactions ICIXC may approach zero. We show that collective interactions are commonplace among widely used organometallics, as well as among boron and aluminum complexes with the general formula [Ma+AR3]b− (A: C, B or Al). In these species, the metal atom interacts more efficiently with the substituents (R) on the central atoms than the central atoms (A) upon forming efficient collective interactions. Furthermore, collective interactions were also found among fluorine atoms of XFn systems (X: B or C). Some of organolithium and organomagnesium species have the lowest ICIXC among the more than 100 studied systems revealing the fact that collective interactions are rather a rule than an exception among organometallic species.

Significant progress has been achieved in enhancing early outcomes for individuals with maple syrup urine disease (MSUD), a rare metabolic disorder that leads to the accumulation of branched‐chain amino acids leucine, isoleucine, and valine, where leucine is known as the primary neurotoxic metabolite. Newborn screening is helpful in early diagnosis and implementation of dietary treatment, thus reducing neurological deterioration and complications in young children. However, patients face the life‐long challenge of maintaining metabolic control through adherence to a strict low‐leucine diet to avoid long‐term consequences of chronic hyperleucinemia, which include cognitive deficits, mood disorders, and movement disorders. This case report exemplifies the complex involvement of MSUD in adult survivors. Despite presenting early in life, the patient thrived until the onset of psychiatric symptoms. The subject of this case is a 25‐year‐old woman with MSUD, who remained in her usual state of health until presentation to the emergency department (ED) with psychosis and altered mental status. However, due to a lack of medical records and poor communication, there was a delay in considering MSUD as a primary cause of her psychiatric symptoms. Although a genetics consultation was later arranged and efforts were made to decrease plasma leucine to the therapeutic range, these interventions proved inadequate in halting her deterioration in health. Her condition worsened within 72 h, culminating in her untimely death. This case emphasizes the comorbidity of psychiatric involvement in MSUD, which contributes to metabolic decompensation that can lead to cerebral edema and death. This case also highlights the pressing need for enhanced strategies for the acute management and long‐term care of MSUD patients with psychiatric involvement, particularly in scenarios where mental disturbance could lead to noncompliance. [ABSTRACT FROM AUTHOR]

The varying literacy standards that undergraduate students represent are a reflection of their interim grasp of academic literacy (Paxton 2007). The aim of this study is to analyse a small group of undergraduate first-year students' depiction of their interim grasp of academic writing and to reflect on how lecturers use formative feedback to respond to specific issues regarding students' academic transitions within their discipline. Using an emancipatory methodology, this study was designed as a participatory action research. Qualitative data from focus group interviews with lecturers and photovoice presentations by first-year dental technology students were used to explore how students demonstrated their interim grasp of academic essay writing in a Health Science department at a university of technology. However, this article, which is part of a broader study, analyses a small class of first-year students' depiction of their interim grasp of essay writing and reflects on how their lecturers use formative feedback to respond to these students' academic transitions within their discipline. Using activity theory and morphogenetic realist theory to analyse essay writing activities during an intervention, the study reveals that images are potent artefacts that students and lecturers use to build meaningful dialogue during essay writing in an uneven terrain.

BackgroundMeasurement of T cell receptor (TCR) or B cell receptor (BCR) gene utilization may be valuable in monitoring the dynamic changes in donor-reactive clonal populations following transplantation and enabling adjustment in therapy to avoid the consequences of excess immune suppression or to prevent rejection with contingent graft damage and to indicate the development of tolerance.ObjectiveWe performed a review of current literature to examine research in immune repertoire sequencing in organ transplantation and to assess the feasibility of this technology for clinical application in immune monitoring.MethodsWe searched MEDLINE and PubMed Central for English-language studies published between 2010 and 2021 that examined T cell/B cell repertoire dynamics upon immune activation. Manual filtering of the search results was performed based on relevancy and predefined inclusion criteria. Data were extracted based on study and methodology characteristics.ResultsOur initial search yielded 1933 articles of which 37 met the inclusion criteria; 16 of these were kidney transplant studies (43%) and 21 were other or general transplantation studies (57%). The predominant method for repertoire characterization was sequencing the CDR3 region of the TCR β chain. Repertoires of transplant recipients were found to have decreased diversity in both rejectors and non-rejectors when compared to healthy controls. Rejectors and those with opportunistic infections were more likely to have clonal expansion in T or B cell populations. Mixed lymphocyte culture followed by TCR sequencing was used in 6 studies to define an alloreactive repertoire and in specialized transplant settings to track tolerance.ConclusionMethodological approaches to immune repertoire sequencing are becoming established and offer considerable potential as a novel clinical tool for pre- and post-transplant immune monitoring.

We introduce a quantum algorithm for memory-efficient biased sampling of rare events generated by classical memoryful stochastic processes. Two efficiency metrics are used to compare quantum and classical resources for rare-event sampling. For a fixed stochastic process, the first is the classical-to-quantum ratio of required memory. We show for two example processes that there exists an infinite number of rare-event classes for which the memory ratio for sampling is larger than r, for any large real number r. Then, for a sequence of processes each labeled by an integer size N, we compare how the classical and quantum required memories scale with N. In this setting, since both memories can diverge as N→, the efficiency metric tracks how fast they diverge. An extreme quantum memory advantage exists when the classical memory diverges in the limit N→, but the quantum memory has a finite bound. We then show that finite-state Markov processes and spin chains exhibit memory advantage for sampling of almost all of their rare-event classes.