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Predicting in vivo response to antineoplastics remains an elusive challenge. We performed a first-of-kind evaluation of two transcriptome-based precision cancer medicine methodologies to predict tumor sensitivity to a comprehensive repertoire of clinically relevant oncology drugs, whose mechanism of action we experimentally assessed in cognate cell lines. We enrolled patients with histologically distinct, poor-prognosis malignancies who had progressed on multiple therapies, and developed low-passage, patient-derived xenograft models that were used to validate 35 patient-specific drug predictions. Both OncoTarget, which identifies high-affinity inhibitors of individual master regulator (MR) proteins, and OncoTreat, which identifies drugs that invert the transcriptional activity of hyperconnected MR modules, produced highly significant 30-day disease control rates (68% and 91%, respectively). Moreover, of 18 OncoTreat-predicted drugs, 15 induced the predicted MR-module activity inversion in vivo. Predicted drugs significantly outperformed antineoplastic drugs selected as unpredicted controls, suggesting these methods may substantively complement existing precision cancer medicine approaches, as also illustrated by a case study. Significance: Complementary precision cancer medicine paradigms are needed to broaden the clinical benefit realized through genetic profiling and immunotherapy. In this first-in-class application, we introduce two transcriptome-based tumor-agnostic systems biology tools to predict drug response in vivo. OncoTarget and OncoTreat are scalable for the design of basket and umbrella clinical trials.

A field experiment was conducted at Sher-e-Bangla Agricultural University, Dhaka during 2014 on transplant aman rice var. BR11 (Mukta) under different levels of chemical fertilizers and green manures to evaluate its grain yield, grain quality and soil fertility status with incorporation of green manures. The experiment was carried out in a split- plot design with three replications. The level of fertilizers (0, 100, 75, 50% of recommended dose, RFD) was placed in main -plot and levels of green manures management (0, 5 and 10 t ha-1 each Mimosa invisa (Lajjaboti) and Sesbania spp. (Dhaincha) in sub-plot. Recommended dose of fertilizers of 83N, 21P, 37K, 11S and 1.5 Zn kg ha-1, were applied in the form of urea, triple super phosphate (TSP), muriate of potash (MoP), gypsum and zinc sulfate, respectively. Sixty-day old Mimosa invisa and Sesbania spp. were in - vitro incorporated before aman transplantation. Combined application of 75% RFD along with Mimosa invisa @ 10 t ha-1 was proved to be the best management in producing higher grain yield (7.25 t ha-1) and that was at par with treatment 50% RFD plus Mimosa invisa @ 10 t ha-1. Quality of grain was increased markedly in respect of amylose, carbohydrate, and protein as well. Furthermore, the treatment improved soil nutrient status in respect of organic matter, nitrogen, sulfur and Zn over no incorporations of green manures. So, addition of green manure like Mimosa invisa could cut fertilizer cost by 25-50%. Bangladesh Agron. J. 2022, 25(2): 67-72

There is a growing interest in the application of machine learning (ML) in Alzheimer’s disease (AD) research. However, neuropsychiatric symptoms (NPS), frequent in subjects with AD, mild cognitive impairment (MCI), and other related dementias have not been analyzed sufficiently using ML methods. To portray the landscape and potential of ML research in AD and NPS studies, we present a comprehensive literature review of existing ML approaches and commonly studied AD biomarkers. We conducted PubMed searches with keywords related to NPS, AD biomarkers, machine learning, and cognition. We included a total of 38 articles in this review after excluding some irrelevant studies from the search results and including 6 articles based on a snowball search from the bibliography of the relevant studies. We found a limited number of studies focused on NPS with or without AD biomarkers. In contrast, multiple statistical machine learning and deep learning methods have been used to build predictive diagnostic models using commonly known AD biomarkers. These mainly included multiple imaging biomarkers, cognitive scores, and various omics biomarkers. Deep learning approaches that combine these biomarkers or multi-modality datasets typically outperform single-modality datasets. We conclude ML may be leveraged to untangle the complex relationships of NPS and AD biomarkers with cognition. This may potentially help to predict the progression of MCI or dementia and develop more targeted early intervention approaches based on NPS.

Previous research in non-dialysis patients suggests that the inflammatory skin disease psoriasis is associated with an increased risk of severe vascular events like myocardial infarction (MI). Thus, we determined whether psoriasis represents a significant risk factor for MI in end-stage renal disease (ESRD) patients.We queried the United States Renal Data System for ESRD patients starting dialysis between 2004 and 2015. ICD-9 and ICD-10 codes were used to identify those with at least two diagnoses of psoriasis, a diagnosis of MI, and other clinical risk factors. Logistic regression was used to examine the association of psoriasis and various risk factors with MI.Of a cohort of 1,062,693, we identified 6,823 (0.6%) subjects with psoriasis and 181,960 (17.1%) with MI. Of the 6,823 patients with psoriasis, 1,671 (24%) developed an MI. Psoriasis was associated with an increased risk of MI in an unadjusted model [odds ratio (OR)=1.34; confidence interval (CI)=1.26-1.42]. However, after controlling for demographics, dialysis modality, access type, and various conditions related to the Charlson Comorbidity Index, psoriasis was not associated with MI (OR=0.95, CI=0.89-1.01). Confounders of the association of psoriasis with MI included congestive heart failure (OR=5.26, CI=5.17-5.36), pulmonary disease (OR=1.25, CI=1.23-1.26), and diabetes with complications (OR=1.82, CI=1.79-1.85).Contrary to prior research in the general population, in the ESRD population psoriasis was not associated with an increased risk of MI after controlling for various demographic and clinical parameters. These data emphasize the importance of an integrated approach since comorbidities may influence the choice of therapy for psoriasis and outcomes.

Background: Lymphatic metastasis in papillary thyroid cancer (PTC) is common; however, the extent of central compartment lymph nodes dissection (CCD) is controversial and requires the knowledge of pattern for central compartment (level VI) lymph nodes metastasis (CCM). Objective: To determine the frequency of central compartment (level VI) lymph node metastasis in T3 & T4 papillary thyroid carcinoma. Methods: This study was conducted in the Department of Otolaryngology-Head &Neck Surgery, BSMMU, Dhaka, from January 2019 to June 2020. A total of 31 cases of T3 & T4 PTC were selected after obtaining clearance and approval from the IRB of BSMMU; the subjects were included based on the inclusion and exclusion criteria. After taking informed written consent detailed history and examination was done. Ultrasound neck and fine-needle aspiration cytology was taken from all thyroid swelling. All patients had underwent total thyroidectomy with central compartment dissection. The data were analyzed by computer-based statistical software SPSS version 26. Results were expressed as frequency and percentage. Z proportion test was done as applicable. Results: In this study, mean (±SD) tumor size was 3.66 (±1.34) cm. 20 (64.52%) had T3 and 11 (35.48%) T4 PTC. 11 (35.48%) had N0, 20 (64.52%) N1a and 8 (25.81%) N1b. 20 (64.52%) had overall central compartment lymph nodes metastasis. Among T3 papillary thyroid carcinoma, 12 (60%) had overall central compartment lymph nodes metastasis, and inT4 papillary thyroid carcinoma, 8 (72.7%) had overall central compartment lymph nodes metastasis. This difference was not statistically significant. Conclusion: The frequency of central compartment (level VI) lymph node metastasis (CCM) is high (64.52%) in T3 & T4 papillary thyroid carcinoma. So central compartment (level VI) should be dissected in advanced (T3 & T4) papillary thyroid carcinoma. Bangladesh J Otorhinolaryngol 2022; 28(2): 149-156

BackgroundObstructive sleep apnoea (OSA) is associated with increased risk of type 2 diabetes. However, results from large population-based prospective cohort studies are rare. The main aim of the present study was to investigate the relative risk of 8-year incident type 2 diabetes in relation to OSA severity in a prospective cohort study of middle-aged and older adults.MethodsA total of 2918 participants (mean age 59 years) of the Korean Genome and Epidemiology Study (KoGES), who underwent home-based overnight polysomnography at baseline examination between 2011 and 2014, were followed up 4-yearly between 2015–2018 and 2019–2021. A total of 1697 participants were present in both follow-ups. After excluding participants who had diabetes at baseline (n=481), a total of 1216 participants were eligible for the analyses.ResultsOSA at baseline was categorised by apnoea–hypopnoea index levels as non-OSA (0–4.9 events·h−1), mild OSA (5.0–14.9 events·h−1) and moderate–severe OSA (≥15.0 events·h−1). Incident type 2 diabetes was identified at each follow-up. Compared with non-OSA, participants with moderate–severe OSA had 1.5 times higher risk of developing type 2 diabetes at the end of the 8-year follow-up after adjusting for potential covariates (relative risk 1.50, 95% CI 1.02–2.21). In the same analytical models for 4-year relative risk of incident type 2 diabetes, none of the OSA groups were at significantly higher risk compared with the non-OSA group.ConclusionModerate–severe OSA, a modifiable risk factor, poses a higher risk of incident type 2 diabetes compared with non-OSA over an 8-year period in general middle-aged and older adults.

Though South Asian economies (SAEs) are progressing by adopting and implementing inclusive financial strategies, many adults, females, those with little or no education, who are not in the labor force, and those from the poorest households are still largely excluded when compared to high-income economies (HIEs). This article intends to explore how promotional inclusive finance is moving forward in the SAEs and whether it helps to converge with the HIEs. Variations in account ownership across SAEs by different characteristics as well as the gaps in account ownership with the HIEs and the globe have also been explored. For understanding and analytical purposes, this study uses the Global Findex Database 2017, the most comprehensive in the world dataset for measuring the extent of inclusive finance in terms of account ownership. Though global recognition of the importance of inclusive finance has been established, the literature lacks a comprehensive study that can identify the gaps in comparing account ownership of the SAEs with the HIEs and the world. This paper intends to fill up these gaps using descriptive statistics on account ownership for transactions, which is the ‘maker’ of financial inclusion.

Effective communication between patients and doctors is fundamental to high-quality healthcare, patient safety, and overall satisfaction. However, the onset of COVID-19 has prompted significant shifts in communication from in-room and face-to-face interactions to virtual consults. The impact of this pandemic-related change on patient-doctor communication goals, processes, attributes, and environment remains unclear. We undertook a scoping review involving the systematic search of seven academic databases for relevant articles published up to and including June 2021. In total, 47 articles were identified that met the inclusion criteria. We applied the patient-doctor communication framework to guide our deductive thematic analysis of articles included, sorting results from reported studies and position papers into themes and sub-themes. The theme of

Urinary tract infection (UTI) caused by uropathogens has put global public health at its utmost risk, especially in developing countries where people are unaware of personal hygiene and proper medication. In general, the infection frequently occurs in the urethra, bladder, and kidney, as reported by the physician. Moreover, many UTI patients whose acquired disorder from the hospital or health-care center has been addressed previously have been referred to as catheter-associated UTI (CAUTI). Meanwhile, the bacterial biofilm triggering UTI is another critical issue, mostly by catheter insertion. In most cases, the biofilm inhibits the action of antibiotics against the UTI-causing bacteria. Therefore, new therapeutic tools should be implemented to eliminate the widespread multidrug resistance (MDR) UTI-causing bacteria. Based on the facts, the present review emphasized the current status of CAUTI, its causative agent, clinical manifestation, and treatment complications. This review also delineated a model of phage therapy as a new therapeutic means against bacterial biofilm-originated UTI. The model illustrated the entire mechanism of destroying the extracellular plyometric substances of UTI-causing bacteria with several enzymatic actions produced by phage particles. This review will provide a complete outline of CAUTI for the general reader and create a positive vibe for the researchers to sort out alternative remedies against the CAUTI-causing MDR microbial agents.

The need for bioresorbable implants that are able to dissolve within the body is rising, unlike their traditional counterparts. Bulk metallic glasses (BMGs) can perhaps serve this need, since they possess incredible properties, including high biocompatibility by virtue of their amorphous structure and absence of dislocations. However, the fabrication of BMGs is challenging, since, to achieve an amorphous structure, fast cooling is a pre-requisite which is very difficult to achieve for casting due to the fact that fast cooling rate and adequate rate of filling of the mold possess a trade-off relationship. Therefore, purpose of this work is to develop a simple novel hybrid approach that is cost effective and attempts to synthesize BMG based on Mg-Ca-Zn constituent. Synthesis of bioresorbable material was attempted by hybridizing friction stir processing (FSP) technique with gas tungsten arc welding (GTAW). FSP was performed with Magnesium as base material and Calcium granules as reinforcement. After FSP, GTAW process was performed by using Zn as filler material. The added Ca and Zn were found to effectively intermix with the Mg matrix in the FSP and GTAW steps, respectively. Especially, a relatively invariable distribution of Ca phases was observed in the stirred microstructure after FSP. Finally, a wide bead consisting of mixed dendritic and columnar cast structure was obtained. The current work is expected to alleviate the physiological issues pertaining to orthopaedic fixations and decrease the need for secondary surgeries in geriatric fractures.

The present study uses Remote Sensing techniques to monitor long-term wheat cultivation in Thakurgaon Sadar Upazila. Multi-spectral Landsat images from 1999 to 2019 at five years intervals were collected at the maximum growth stage of wheat. The images were processed by QGIS, ArcGIS, and R software with the random forest supervised classification. The findings revealed that images were classified and separated the crops successfully due to cloud-free images and pure pixels. The results show that the wheat area was decreased from 1999 (16349 ha) to 2019 (9161 ha). It was due to the transformation of the wheat area into other crop areas. The shrinkage rate of wheat areas was much higher (10.93 %) in 1999-09 than in 2009-19. It was due to a sudden decline in blast disease during 2005-09 and increased again. The shrinkage of the wheat area has been driven mainly by climate change influencing profitability. Prolonged hydrological drought introduced maize and potato in the wheat area. The benefit-cost ratio decreased gradually in wheat but increased in maize and potato. The study demonstrates that remote sensing is an effective method for wheat crop area monitoring. This study will help us understand the status of long-term wheat cultivation. Environ. Sci. & Natural Resources, 13(1&2): 25-37, 2020

Recent psychological and neurological studies suggest that human motor preparation and execution are largely affected by the subjective emotional state. Thus, external emotion stimuli can be a potential tool to enhance the detectability of movement intention from pre-movement neural signals. This study investigated whether emotion-evoking music stimulus could improve the performances of a fully predictive Brain-Computer Interface (BCI) system for movement intention detection. For this purpose, electroencephalographical (EEG) signals were recorded from twelve healthy subjects under three emotional conditions: happy, sad, and neutral. The emotions were elicited using external music stimuli while they performed a wrist extension action. Additionally, support vector machine-based offline and pseudo online testing schemes were employed to solve a binary classification problem for determining movement intention from pre-movement EEG. EEG power analysis showed that happy music stimulus resulted in an early occurrence of event-related desynchronization in alpha band compared other emotional states. Happy emotional stimuli also resulted in comparatively better performances in both offline and pseudo online testing paradigms. The results of this study suggest that external happy music stimulus could enhance the early and accurate detectability of human self-paced movement intention and thus could contribute to the predictive capability of state-of-the-art assistive BCIs.

Viral hepatitis (B and C) has become silent and life-threatening epidemic in recent years. Liver cirrhosis and liver carcinoma are primarily caused by this condition. In 2019, Hepatitis B resulted in an estimated 1.5 million new infections and 820,000 deaths mostly due to cirrhosis and hepatocellular carcinoma. There are “n” number of ways, in which infections can be triggered in a Dentists Chamber through direct or indirect contact with human blood, secretions, and aerosols. To reduce the hepatitis infection in their community, the aim of this research is to raise awareness among dental clinicians. Several elements connected with compliance to the hepatitis vaccination were not known by dentists, which placed them among the high-risk groups. Dental professionals can play a significant role in preventing hepatitis by recognizing that every patient has the potential to transmit the disease. For the prevention of this viral infection, sterilization, and prophylactic vaccination routine should be adhered properly. Our search keywords included hepatitis, dental, liver disease, and infection control from online databases such as ProQuest, PubMed, and Medline. In addition to online databases, we searched various journals and books by hand. The focus of this review was only on highly relevant English literature.

In this paper, an autonomous brain tumor segmentation and detection model is developed utilizing a convolutional neural network technique that included a local binary pattern and a multilayered support vector machine. The detection and classification of brain tumors are a key feature in order to aid physicians; an intelligent system must be designed with less manual work and more automated operations in mind. The collected images are then processed using image filtering techniques, followed by image intensity normalization, before proceeding to the patch extraction stage, which results in patch extracted images. During feature extraction, the RGB image is converted to a binary image by grayscale conversion via the colormap process, and this process is then completed by the local binary pattern (LBP). To extract feature information, a convolutional network can be utilized, while to detect objects, a multilayered support vector machine (ML-SVM) can be employed. CNN is a popular deep learning algorithm that is utilized in a wide variety of engineering applications. Finally, the classification approach used in this work aids in determining the presence or absence of a brain tumor. To conduct the comparison, the entire work is tested against existing procedures and the proposed approach using critical metrics such as dice similarity coefficient (DSC), Jaccard similarity index (JSI), sensitivity (SE), accuracy (ACC), specificity (SP), and precision (PR).

The study presents a new approach for solving the sensor subset selection problem using set encoding and genetic algorithms, aiming to minimize the number of sensors while maintaining accurate spatial estimation. The proposed method, tested on groundwater data from the Savannah River Site, introduces novel crossover and mutation methods, outperforming a previous greedy method with an R2 higher than 0.98 for reduced sensor counts.

Background Flavonols are phytoconstituents of biological and medicinal importance. In addition to functioning as antioxidants, flavonols may play a role in antagonizing diabetes, cancer, cardiovascular disease, and viral and bacterial diseases. Quercetin, myricetin, kaempferol, and fisetin are the major dietary flavonols. Quercetin is a potent scavenger of free radicals, providing protection from free radical damage and oxidation-associated diseases. Main body of the abstract An extensive literature review of specific databases (e.g., Pubmed, google scholar, science direct) were conducted using the keywords “flavonol,” “quercetin,” “antidiabetic,” “antiviral,” “anticancer,” and “myricetin.” Some studies concluded that quercetin is a promising antioxidant agent while kaempferol could be effective against human gastric cancer. In addition, kaempferol prevents apoptosis of pancreatic beta-cells via boosting the function and survival rate of the beta-cells, leading to increased insulin secretion. Flavonols also show potential as alternatives to conventional antibiotics, restricting viral infection by antagonizing the envelope proteins to block viral entry. Short conclusion There is substantial scientific evidence that high consumption of flavonols is associated with reduced risk of cancer and coronary diseases, free radical damage alleviation, tumor growth prevention, and insulin secretion improvement, among other diverse health benefits. Nevertheless, more studies are required to determine the appropriate dietary concentration, dose, and type of flavonol for a particular condition to prevent any adverse side effects.

Background The bitter gourd fruit yields are not at a desirable level due to maleness. Maleness is one of the biggest difficulties with bitter gourd, reducing fruit yields substantially. On the other hand, plant stages are the most important consideration for PGR application because of their sensitive responsiveness. Therefore, it is necessary to determine how bitter gourd plants respond to plant growth regulators, namely PGR0 (control: application of water as control treatment), PGR1 (GA3-Gibberellic acid, 100 mgL−1), PGR2 (NAA-Naphthalene acetic acid, 100 mgL−1), and PGR3 (MH- Maleic hydrazide, 100 mgL−1) at three application phases, S1: soaking of seeds; S2: four-leaf; and S3: flower bud in terms of vegetative development, male–female flower sex alteration, and fruit features including bioactive compounds. Results Physiological, floral and yield characteristics of bitter gourd exposed significant changes by GA3 and MH at various stages. The number of leaves plant−1, the fresh mass of the plant, and the fresh mass of fruit and fruit dry matter content were enhanced remarkably for MH application at the seed soaking stage except for plant height and the number of branches plant−1. MH increased the number of female flowers plant−1, the number of fruits plant−1 and the yield at the 4-leaf stage of application. PGRs substantially influenced the quantity of sugar, chlorophyll, total carotenoids, protein, and water content at various phases of their application but not on ascorbic acid, TSS, or total phenol concentration. Conclusions For sex expression and yield attributes, 100 mgL−1 MH at the 4-leaf stage would be effective in bitter gourd. Exogenous NAA showed a considerable dual influence on plant development and antioxidant enzyme activity in bitter gourd fruits.

Background: Bioautography is a technique for the detection of biological activity that combines the elements of planar chromatography. Its hyphenated variants are widely used in the screening of natural products possessing biological activity. It can be used in the activity-based screening of phytochemical ingredients by employing various enzyme processes and reactions and facilitates the rapid determination of bioactive compounds in pant samples. Objective: To give a comprehensive overview of effect-directed assays and biological detection approaches used in conjugation with thin layer chromatography technique. The present review article attempts to throw light on the various aspects of bioautography, including its types and applications, thereby giving its concise overview and its relevance in the field of natural product screening. Methods: Various search engines were used for the literature survey, including Google Scholar, Semantic Scholar, PubMed, ResearchGate and Scopus. Results: Bioautography has wide-ranging uses in the screening of compounds such as antioxidants, antifungals, antimicrobials, estrogenic, antitumors, and various enzyme inhibitors compounds like α and β-glucosidase inhibitors and α-amylase inhibitors. Conclusion: Bioautography serves to be an effective tool for the isolation of bioactive phytochemicals, thereby allowing us to scientifically validate the biological activities of various compounds, which can then be utilized for making potent medications for various diseases.

Supplementary figures and tables

supplemental figure legends

Figure S1. Components of EnCore-A; Figure S2. Silencing of CTNNB1 mRNA after single-dose or multiple-dose regimens; Figure S3: Comparable activity achieved with a payload with 2'F modifications; Figure S4. Tumor growth inhibition in multiple subcutaneous xenograft models; Figure S5. No efficacy observed in APC/CTNNB1 wild-type RKO tumors; Figure S6. ApoE-independent DsiRNA delivery to primary and metastatic tumors; Figure S7. Detection of cleaved CTNNB1 mRNA by 5'RACE-Seq; Figure S8. EnCore-mediated delivery to CTNNB1/YAP-driven hepatoblastoma; Figure S9: Tolerability profile of EnCore-R/CTNNB1 DsiRNA in nonhuman primates.

BackgroundLuciferase is a well-known oxidative enzyme that produces bioluminescence. ThePseudomonas meliaeis a plant pathogen that causes wood rot on nectarine and peach and possesses a luciferase-like monooxygenase. After activation, it produces bioluminescence, and the pathogen’s bioluminescence is a visual indicator of diseased plants.MethodsThe present study aims to model and characterize the luciferase-like monooxygenase protein inP. meliaefor its similarity to well-established luciferase. In this study, the luciferase-like monooxygenase fromP. meliaeinfects chinaberry plants has been modeled first and then studied by comparing it with existing known luciferase. Also, the similarities between uncharacterized luciferase fromP. meliaeand template fromGeobacillus thermodenitrificanswere analyzed to find the novelty ofP. meliae.ResultsThe results suggest that the absence of bioluminescence inP. meliaecould be due to the evolutionary mutation in positions 138 and 311. The active site remains identical except for two amino acids;P. meliaeTyr138 instead of His138 and Leu311 instead of His311. Therefore, theP. meliaewill have a potential future application, and mutation of the residues 138 and 311 can be restored luciferase light-emitting ability.ConclusionsThis study will help further improve, activate, and repurpose the luciferase fromP. meliaeas a reporter for gene expression.

The ability to classify images accurately and efficiently is dependent on having access to large labeled datasets and testing on data from the same domain that the model is trained on. Classification becomes more challenging when dealing with new data from a different domain, where collecting a large labeled dataset and training a new classifier from scratch is time-consuming, expensive, and sometimes infeasible or impossible. Cross-domain classification frameworks were developed to handle this data domain shift problem by utilizing unsupervised image-to-image (UI2I) translation models to translate an input image from the unlabeled domain to the labeled domain. The problem with these unsupervised models lies in their unsupervised nature. For lack of annotations, it is not possible to use the traditional supervised metrics to evaluate these translation models to pick the best-saved checkpoint model. In this paper, we introduce a new method called Pseudo Supervised Metrics that was designed specifically to support cross-domain classification applications contrary to other typically used metrics such as the FID which was designed to evaluate the model in terms of the quality of the generated image from a human-eye perspective. We show that our metric not only outperforms unsupervised metrics such as the FID, but is also highly correlated with the true supervised metrics, robust, and explainable. Furthermore, we demonstrate that it can be used as a standard metric for future research in this field by applying it to a critical real-world problem (the boiling crisis problem)., arXiv admin note: text overlap with arXiv:2212.09107

Spin triplet superconductor UTe$_{2}$ is widely believed to host a quasi-two-dimensional Fermi surface, revealed by first principal calculations, photoemission and quantum oscillation measurements. An outstanding question still remains as to the existence of a three-dimensional Fermi surface pocket, which is crucial for our understanding of the exotic superconducting and topological properties of UTe$_{2}$. This 3D Fermi surface pocket appears in various theoretical models with different physics origins but has not been detected experimentally. Here for the first time, we provide concrete evidence for a relatively isotropic, small Fermi surface pocket of UTe$_{2}$ via quantum oscillation measurements. In addition, we observed high frequency quantum oscillations corresponding to electron-hole tunneling between adjacent electron and hole pockets. The coexistence of 2D and 3D Fermi surface pockets, as well as the breakdown orbits, provides a test bed for theoretical models and aid the realization of a unified understanding of superconducting state of UTe$_{2}$ from the first-principles approach.