Identification of Novel Biomarker and Therapeutic Target Candidates for Diagnosis and Treatment of Follicular Adenoma

ollicular adenoma is a type of benign and encapsulated nodule in the thyroid gland, but some adenomas have the potential to progress to follicular carcinoma. Therefore, it is important to monitor the state and progress of follicular adenoma in the clinic and discover drug development targets for the treatment of follicular adenoma to prevent its worsening to follicular carcinoma. Currently, the study of biomarkers and therapeutic targets lacks applications of up-to-date technologies, including proteomics and bioinformatics. To discover novel protein biomarker and therapeutic target candidates, a liquid chromatography-tandem mass spectrometry approach was applied to directly compare follicular adenoma with normal thyroid tissue samples. The proteomics analysis revealed 114 protein biomarker candidates out of 1,780 identified and quantified proteins. A comprehensive approach to prioritize the biomarker candidates by category and rank revealed CD63, DDB1, TYMP, VDAC2, and DCXR as the top five biomarker candidates. Upstream regulator analysis using the Ingenuity Pathway Analysis (IPA) software discovered four therapeutic target candidates for follicular adenoma, including TGFB1, MYC, ANGPT2, and NFE2L2. This study provided biomarker and therapeutic target candidates for a follow-up study, which will facilitate monitoring and treatment of follicular adenoma

Identification of novel biomarker candidates for immunohistochemical diagnosis to distinguish low-grade chondrosarcoma from enchondroma

Chondrosarcoma is the third most common primary bone cancer, requiring surgical resection. However, differentiation of low-grade chondrosarcoma (grade 1) from enchondroma that is benign and only requires regular follow-up is one of the most frequent diagnostic dilemmas facing orthopedic oncologists in clinical management. Although multiple techniques are applied to make the distinction, immunohistochemistry is an important ancillary technique, especially when a histopathological stain of specimen must be obtained in order to guarantee an accurate confirmation. Currently, no adequate immunohistochemical diagnostic protein biomarkers are available to distinguish low-grade chondrosarcoma from enchondroma. To discover novel protein biomarker candidates, an LC-MS/MS approach was applied to directly compare formalin-fixed, paraffin-embedded low-grade chondrosarcoma with enchondroma tissue samples. The proteomics analysis revealed 17 protein biomarker candidates. A principle was developed to prioritize the candidates using category and ranking. An algorithm, prioritization index of biomarker candidates for immunohistochemistry on tissue specimens, was developed to rank the candidates inside each category. Using the proteomics data and bioinformatics results, the prioritization index of biomarker candidates for immunohistochemistry on tissue revealed periostin as a top candidate. Immunohistochemical staining of periostin in 23 low-grade chondrosarcoma and 31 enchondroma tissue specimens disclosed 87% specificity and 70% sensitivity

Acute appendicitis caused by acute myeloid leukemia

KEY CLINICAL MESSAGE: A case of appendiceal involvement by acute myeloid leukemia (AML) in an adult with recent history of AML transformed from myelodysplastic syndrome (MDS) was presented. Being aware of this rare presentation in particular in a patient with history of MDS and/or AML is important for prompt clinical diagnosis and management

World Health Organization (WHO)/International Society of Urological Pathology (ISUP) grading in fine‐needle aspiration biopsies of renal masses

Background Utilization of fine‐needle aspiration (FNA) biopsy for the evaluation of renal masses has been increasing at our institution. At times diagnostic material on direct smears is superior to that in the cell block/core biopsy, therefore assigning an accurate nuclear grade in the cytopathology report would provide useful prognostic information. Methods Search of the pathology database identified renal FNAs performed during an 11‐year period (2006–2017). Corresponding core biopsies and resections were identified. Cases with a diagnosis of primary renal neoplasia on FNA, core biopsy, and/or resection were included. Two pathologists reviewed all cases and assigned a World Health Organization (WHO)/International Society of Urological Pathology (ISUP) grade to each FNA, core biopsy, and resection case. Results A total of 162 kidney FNAs were identified. Primary renal neoplasia was diagnosed in 137 cases on core biopsy/resection. Among diagnostic FNAs of clear cell RCC and papillary RCC with core biopsy/resection specimens for re‐review (n = 52), reviewers assigned a concordant WHO/ISUP grade to 83% (43/52) of cases. Among 9 cases with discrepant scores, all had a discrepancy of 1 grade and were undergraded on FNA. Using a two tier grading system (low vs. high grade), reviewers assigned a concordant grade to 88% (46/52) of cases. Among 6 cases with discrepant scores, all were classified as low grade (WHO/ISUP grade 2) on FNA versus high grade (WHO/ISUP grade 3) on resection. Conclusion The WHO/ISUP grade assigned on FNA shows good concordance with subsequent resection/core specimens (83%), with all discrepant cases being undergraded by one grade

Formulation and evaluation of transdermal drug-delivery system of isosorbide dinitrate

The purpose of this study was to develop a reservoir-type transdermal delivery system for isosorbide dinitrate (ISDN). The developed patch consisted of five layers from bottom to top, namely, a temporary liner, an adhesive layer, a rate-controlling membrane, a reservoir and a backing. The effects of chemical penetration enhancers, reservoir materials and rate-controlling membranes on the release behaviour of ISDN from the transdermal patch were studied, and the; in vitro; release of ISDN from the developed patch was studied and compared with the commercially available ISDN patch. The results showed that there was no significant difference in permeation rates between the developed reservoir-type patch and the commercially available ISDN patch (;p;>; 0.05). Moreover, the cumulative release ratio of the commercially available ISDN patch in 48 h was up to 89.8%, whereas the developed patch was only 34.9%, which meant the sustained release time of the developed patch was much longer than the commercially available ISDN patch, and would promote the satisfaction of the patient.;O objetivo do presente estudo foi desenvolver um sistema de liberação transdérmico do tipo reservatório para o dinitrato de isossorbida (ISDN, abrevitura em Inglês). A formulação transdérmica desenvolvida constou de cinco camadas de baixo para cima, ou seja, um revestimento temporário, uma camada adesiva, uma membrana controladora da taxa de liberação, um reservatório e um reforço. Estudaram-se os efeitos dos potenciadores de penetração química, materiais do reservatório e membranas de controle da taxa de liberação no comportamento da formulação transdérmica de dinitrato de isossorbida. A liberação; in vitro; da formulação transdérmica de dinitrato de isossorbida desenvolvida foi estudada em comparação com a formulação de dinitrato de isossorbida disponível comercialmente. Os resultados mostraram que não existem diferenças significativa nas taxas de permeação entre o tipo de reservatório desenvolvido e o de dinitrato de isossorbida desenvolvido comercialmente (;p;>;0,05). Ademais, a taxa de liberação cumulativa da formulação de dinitrato de isossorbida disponível comercialmente em 48 horas foi de até 89,8% e a da formulação desenvolvida, de apenas de 34,9%, o que provou que a liberação sustentada da formulação desenvolvida foi muito maior do que a de dinitrato de isossorbida desenvolvida comercialmente, o que promoveria a satisfação do paciente.

Posterior-GAN: Towards Informative and Coherent Response Generation with Posterior Generative Adversarial Network

Neural conversational models learn to generate responses by taking into account the dialog history. These models are typically optimized over the query-response pairs with a maximum likelihood estimation objective. However, the query-response tuples are naturally loosely coupled, and there exist multiple responses that can respond to a given query, which leads the conversational model learning burdensome. Besides, the general dull response problem is even worsened when the model is confronted with meaningless response training instances. Intuitively, a high-quality response not only responds to the given query but also links up to the future conversations, in this paper, we leverage the query-response-future turn triples to induce the generated responses that consider both the given context and the future conversations. To facilitate the modeling of these triples, we further propose a novel encoder-decoder based generative adversarial learning framework, Posterior Generative Adversarial Network (Posterior-GAN), which consists of a forward and a backward generative discriminator to cooperatively encourage the generated response to be informative and coherent by two complementary assessment perspectives. Experimental results demonstrate that our method effectively boosts the informativeness and coherence of the generated response on both automatic and human evaluation, which verifies the advantages of considering two assessment perspectives.Comment: Accepted by AAAI 202

Monolingual or Multilingual Instruction Tuning: Which Makes a Better Alpaca

Foundational large language models (LLMs) can be instruction-tuned to develop open-ended question-answering capability, facilitating applications such as the creation of AI assistants. While such efforts are often carried out in a single language, building on prior research, we empirically analyze cost-efficient approaches of monolingual and multilingual tuning, shedding light on the efficacy of LLMs in responding to queries across monolingual and multilingual contexts. Our study employs the Alpaca dataset and machine translations of it to form multilingual training data, which is then used to tune LLMs through low-rank adaptation and full-parameter training. Comparisons reveal that multilingual tuning is not crucial for an LLM's English performance, but is key to its robustness in a multilingual environment. With a fixed budget, a multilingual instruction-tuned model, merely trained on downsampled data, can be as powerful as training monolingual models for each language. Our findings serve as a guide for expanding language support through instruction tuning with constrained computational resources.Comment: Work in progres

Adaptive fuzzy sliding mode algorithm-based decentralised control for a permanent magnet spherical actuator

<p>The dynamic model of multi-degree-of-freedom permanent magnet (PM) spherical actuators is multivariate and nonlinear due to strong inter-axis couplings, which affects the trajectory tracking performance of the system. In this paper, a decentralised control strategy based on adaptive fuzzy sliding mode (AFSM) algorithm is developed for a PM spherical actuator to enhance its trajectory tracking performance. In this algorithm, the coupling terms are separated as subsystems from the entire system. The AFSM algorithm is applied in such a way that the fuzzy logic systems are used to approximate the subsystem with uncertainties. A sliding mode term is introduced to compensate for the effect of coupling terms and fuzzy approximation error. The stability of the proposed method is guaranteed by choosing the appropriate Lyapunov function. Both simulation and experimental results show that the proposed control algorithm can effectively handle various uncertainties and inter-axis couplings, and improve the trajectory tracking precision of the spherical actuator.</p