Non-coding RNA as future target for diagnose and treatment of perineural invasion in cancers

Perineural invasion(PNI),a particularly insidious form of tumor metastasis distinct from hematogenous or lymphatic spread,has the capacity to extend well beyond the primary tumor site,infiltrating distant regions devoid of lymphatic or vascular structures.PNI often heralds a decrease in patient survival rates and is recognized as an indicator of an unfavorable prognosis across a variety of cancers.Despite its clinical significance,the underlying molecular mechanisms of PNI remain elusive,complicating the development of specific and efficacious diagnostic and therapeutic strategies.In the realm of cancer research,non-coding RNAs(ncRNAs)have attracted considerable attention due to their multifaceted roles and cancer-specific expression profiles,positioning them as promising candidates for applications in cancer diagnostics,prognostics,and treatment.Among the various types of ncRNAs,microRNAs(miRNAs),long non-coding RNAs(lncRNAs),and circular RNAs(circRNAs)have emerged as influential players in PNI.Their involvement is increasingly recognized as a contributing factor to tumor progression and therapeutic resistance.Our study synthesizes and explores the diverse functions and mechanisms of ncRNAs in relation to PNI in cancer.This comprehensive review aims to shed light on cutting-edge perspectives that could pave the way for innovative diagnostic and therapeutic approaches to address the challenges posed by PNI in oncology.

Construction of the underlying circRNA-miRNA-mRNA regulatory network and a new diagnostic model in ulcerative colitis by bioinformatics analysis

BACKGROUND Circular RNAs(circRNAs)are involved in the pathogenesis of many diseases through competing endogenous RNA(ceRNA)regulatory mechanisms.AIM To investigate a circRNA-related ceRNA regulatory network and a new predictive model by circRNA to understand the diagnostic mechanism of circRNAs in ulcerative colitis(UC).METHODS We obtained gene expression profiles of circRNAs,miRNAs,and mRNAs in UC from the Gene Expression Omnibus dataset.The circRNA-miRNA-mRNA network was constructed based on circRNA-miRNA and miRNA-mRNA interactions.Functional enrichment analysis was performed to identify the biological mechanisms involved in circRNAs.We identified the most relevant differential circRNAs for diagnosing UC and constructed a new predictive nomogram,whose efficacy was tested with the C-index,receiver operating characteristic curve(ROC),and decision curve analysis(DCA).RESULTS A circRNA-miRNA-mRNA regulatory network was obtained,containing 12 circRNAs,three miRNAs,and 38 mRNAs.Two optimal prognostic-related differentially expressed circRNAs,hsa_circ_0085323 and hsa_circ_0036906,were included to construct a predictive nomogram.The model showed good discrimination,with a C-index of 1(>0.9,high accuracy).ROC and DCA suggested that the nomogram had a beneficial diagnostic ability.CONCLUSION This novel predictive nomogram incorporating hsa_circ_0085323 and hsa_circ_0036906 can be conveniently used to predict the risk of UC.The circRNa-miRNA-mRNA network in UC could be more clinically significant.

Perivascular epithelioid cell tumors of the liver misdiagnosed as hepatocellular carcinoma:Three case reports

BACKGROUND Hepatic perivascular epithelioid cell neoplasms(PEComas)are rare.Diagnostic and treatment experience with hepatic PEComa remains insufficient.CASE SUMMARY Three hepatic PEComa cases are reported in this paper:One case of primary malignant hepatic PEComa,one case of benign hepatic PEComa,and one case of hepatic PEComa with an ovarian mature cystic teratoma.During preoperative imaging and pathological assessment of intraoperative frozen samples,patients were diagnosed with hepatocellular carcinoma(HCC),while postoperative pathology and immunohistochemistry subsequently revealed hepatic PEComa.Patients with hepatic PEComa which is misdiagnosed as HCC often require a wider surgical resection.It is easy to mistake them for distant metastases of hepatic PEComa and misdiagnosed as HCC,especially when it’s combined with tumors in other organs.Three patients eventually underwent partial hepatectomy.After 1-4 years of follow-up,none of the patients experienced recurrence or metastases.CONCLUSION A clear preoperative diagnosis of hepatic PEComa can reduce the scope of resection and prevent unnecessary injuries during surgery.

Prediction System for Diagnosis and Detection of Coronavirus Disease-2019(COVID-19):A Fuzzy-Soft Expert System

In early December 2019,a new virus named“2019 novel coronavirus(2019-nCoV)”appeared in Wuhan,China.The disease quickly spread worldwide,resulting in the COVID-19 pandemic.In the currentwork,we will propose a novel fuzzy softmodal(i.e.,fuzzy-soft expert system)for early detection of COVID-19.Themain construction of the fuzzy-soft expert systemconsists of five portions.The exploratory study includes sixty patients(i.e.,fortymales and twenty females)with symptoms similar to COVID-19 in(Nanjing Chest Hospital,Department of Respiratory,China).The proposed fuzzy-soft expert systemdepended on five symptoms of COVID-19(i.e.,shortness of breath,sore throat,cough,fever,and age).We will use the algorithm proposed by Kong et al.to detect these patients who may suffer from COVID-19.In this way,the present system is beneficial to help the physician decide if there is any patient who has COVID-19 or not.Finally,we present the comparison between the present system and the fuzzy expert system.

COVID TCL:A Joint Metric Loss Function for Diagnosing COVID-19 Patient in the Early and Incubation Period

Convolution Neural Networks(CNN)can quickly diagnose COVID-19 patients by analyzing computed tomography(CT)images of the lung,thereby effectively preventing the spread of COVID-19.However,the existing CNN-based COVID-19 diagnosis models do consider the problem that the lung images of COVID-19 patients in the early stage and incubation period are extremely similar to those of the non-COVID-19 population.Which reduces the model’s classification sensitivity,resulting in a higher probability of the model misdiagnosing COVID-19 patients as non-COVID-19 people.To solve the problem,this paper first attempts to apply triplet loss and center loss to the field of COVID-19 image classification,combining softmax loss to design a jointly supervised metric loss function COVID Triplet-Center Loss(COVID-TCL).Triplet loss can increase inter-class discreteness,and center loss can improve intra-class compactness.Therefore,COVID-TCL can help the CNN-based model to extract more discriminative features and strengthen the diagnostic capacity of COVID-19 patients in the early stage and incubation period.Meanwhile,we use the extreme gradient boosting(XGBoost)as a classifier to design a COVID-19 images classification model of CNN-XGBoost architecture,to further improve the CNN-based model’s classification effect and operation efficiency.The experiment shows that the classification accuracy of the model proposed in this paper is 97.41%,and the sensitivity is 97.61%,which is higher than the other 7 reference models.The COVID-TCL can effectively improve the classification sensitivity of the CNN-based model,the CNN-XGBoost architecture can further improve the CNN-based model’s classification effect.

A Validated Model for the Imaging Diagnosis of Cystic Lung Disease

Rationale and Objectives: Cystic lung disease may be accurately diagnosed by imaging interpretation of specialist radiologists, without other information. We hypothesized that with minimal training non-specialists could perform similarly to specialist physicians in the diagnosis of cystic lung disease. Methods: 72 cystic lung disease cases and 25 cystic lung disease mimics were obtained from three sources: 1) a prospective acquired diffuse lung disease registry, 2) a retrospective search of medical records and 3) teaching files. Cases were anonymized, randomized and interpreted by 7 diffuse lung disease specialists and 15 non-specialist radiologists and pulmonologists. Clinical information other than age and sex was not provided. Prior to interpretation, non-specialists viewed a short PDF training document explaining cystic lung disease interpretation. Results: Correct first choice diagnosis of 85%-88% may be achieved by high-performing specialist readers and 71%-80% by non-specialists and lower-performing specialists, with mean accuracies in the diagnosis of LAM (91%, p Conclusion: With specific but limited training, non-specialist physicians can diagnose cystic lung diseases from CT appearance alone with similar accuracy to specialists, correctly identifying approximately 75% of cases.

A novel conditional diagnosability algorithm under the PMC model

Conditionally t-diagnosable and t-diagnosable are important in system level diagnosis. Therefore,it is valuable to identify whether the system is conditionally t-diagnosable or t-diagnosable and derive the corresponding conditional diagnosability and diagnosability. In the paper,distinguishable measures of pairs of distinct faulty sets with a new perspective on establishing functions are focused.Applying distinguishable function and decision function,it is determined whether a system is conditionally t-diagnosable( or t-diagnosable) or not under the PMC( Preparata,Metze,and Chien)model directly. Based on the decision function,a novel conditional diagnosability algorithm under the PMC model is introduced which can calculate conditional diagnosability rapidly.

Petri net model for diagnosis of permanent faults of a hydraulic system

Petri net model is applied to diagnose the permanent fault of hydraulic system within the framework of interpreted Petri net. The permanent fault is described as redundant structure of the model. A definition and a theorem are proposed to determine the diagnosability of the hydraulic system. The relations bwtween the diagnosability and other structure properties are also discussed. An example of actual hydraulic system is presented and its permanent fault can be diagnosed by the proposed method efficiently.

THEORY ON THE DIAGNOSABILITY OF THE FAULT CLASSIFICATION APPROACH

This paper describes, by means of a Voronoi hypersphere, the nearest neighbor relations of all the feature submatrices in the fault classification space and analyses the deviation disturbance angles between fault feature submatrices and a k-dimension unitary matrix of the measured voltage change matrix. With the above two concepts, this paper discusses the diagnos-ability in the fault classification approach. The paper also classifies and defines the fault diagnosis problems. Finally, the paper derives the corresponding necessary and sufficient conditions for correct location of faults.

Micro-piezoelectric pulse diagnoser and frequency domain analysis of human pulse signals

Background:The theory of pulse diagnosis is to assess the physiological condition of the human body using radial pulse.However,pulses can vary markedly from person to person.Further,pulse diagnosis is difficult to learn and requires one-on-one teaching.Methods:To address this problem,we built a home-made pulse diagnoser and measured human pulses for studying the standardization of pulse diagnosis.Our pulse diagnoser was composed of a piezoelectric transducer,differential amplifier,data acquisition instrument,and a Matlab analysis program.The measured pulses were converted into electronic signals by a piezoelectric transducer and saved on a computer.The digitalized data were then refined and analyzed by fast Fourier transform for frequency analysis.Simulations were performed to assess the factors that affected the pulse,including phase shifts of reflected pulse waves (generate sub-peaks in pulses),inconsistent heart rates (deform pulse waves),the vessel stiffness (alter harmonic frequencies of the pulses),and the wave amplitudes (determined by the pulse strength).Results:By comparing a published report and our simulation findings,we characterized the pulse types and the effects of various factors,and then applied the findings to study actual pulses in patients.Three types of pulses were determined from the frequency spectrumchoppy pulse (Se mai) without apparent harmonic peaks,the harmonic frequencies of wiry pulse (Xian mai) that are non-integer multiples of the fundamental frequency,and surging pulse (Hong mai) that exhibit strong amplitudes in the spectrum of frequency.A normal pulse and a slippery pulse were differentiated by a phase shift,but not by assessing the frequency spectrum.Conclusion:These findings confirm that frequency domain analysis can avoid ambiguity arising in assessing the three types of pulses in the time domain.Further studies of other pulses in the frequency domain are required to develop a precise electronic pulse diagnoser.

Validation of a Questionnaire to Diagnose the Baumann Skin Type in All Ethnicities and in Various Geographic Locations

The Baumann Skin Typing System diagnoses patients as having one of 16 skin types based on their answers to a validated questionnaire [i] known as the Baumann Skin Type Indicator [ii]. The BSTI questionnaire has been tested over the last decade on over 200,000 people of various ages and ethnicities in different geographic locations around the world. In this study, data were collected from 52,862 patients to compare skin type prevalence between those who presented to doctor’s offices and those who took the quiz without supervision online. The most common skin types varied only slightly between patients that took the quiz online and those that completed the questionnaire in their doctor’s office. This indicates that the prevalence of skin types seen in the doctor’s office is similar to that in the general population and that supervision is not necessary to get an accurate result on the BSTI. [iii] In addition, comparison of data gathered in China, Korea, and the US did not show a significant difference in skin type prevalence between Asian and Caucasian skin types. [iv] This study demonstrates that the English version of the BSTI is valid for English speaking patients online, and in doctors’ offices in the US, China and Korea.

Serum uric acid level in newly diagnosed essential hypertension in a Nepalese population:A hospital based cross sectional study

Objective:To develop the missing link between hyperuricemia and hypertension.Methods:The study was conducted in Department of Biochemistry in collaboration with Nephrology Unit of Internal Medicine Department.Hypertension was defined according to blood pressure readings by definitions of the Seventh Report of the Joint National Committee.Totally 205newly diagnosed and untreated essential hypertensive cases and age-sex matched nonnotensive controls were enrolled in the study.The potential confounding factors of hyperuricemia and hypertension in both cases and controls were controlled.Uric acid levels in all participants were analyzed.Results:Renal function between newly diagnosed hypertensive cases and nonnotensive healthy controls were adjusted.The mean serum uric acid observed in newly diagnosed hypertensive cases and in nonnotensive healthy controls were(290.05±87.03)μmol/L and(245.24±09.38)μmol/L respectively.A total of 59(28.8%)participants of cases and 28(13.7%)participants of controls had hyperuricemia(odds ratio 2.555(95%CI:1.549-4.213),P<0.00l).Conclusions:The mean serum uric acid leveb and number of hyperuricemic subjects were found to be significantly higher in cases when compared to controls.

The 1-Good-neighbor Connectivity and Diagnosability of Locally Twisted Cubes

Diagnosability of a multiprocessor system is one important study topic. In 2012, Peng et al. proposed the g-good-neighbor diagnosability that restrains every fault-free node to contain at least g fault-free neighbors. The locally twisted cube LTQ_n has many good properties. In this paper, we show that the 1-good-neighbor connectivity κ~1(LTQ_n) = 2n-2 and the 1-good-neighbor diagnosability of LTQ_n is 2n-1 under the PMC model for n ≥ 4 and the MM~*model for n ≥ 5.

Evaluation of fault diagnosability for nonlinear uncertain systems with multiple faults occurring simultaneously

Up to present,the problem of the evaluation of fault diagnosability for nonlinear systems has been investigated by many researchers.However,no attempt has been done to evaluate the diagnosability of multiple faults occurring simultaneously for nonlinear systems.This paper proposes a method based on differential geometry theories to solve this problem.Then the evaluation of fault diagnosability for affine nonlinear systems with multiple faults occurring simultaneously is achieved.To deal with the effect of control laws on the evaluation results of fault diagnosability,a design scheme of the evaluation of fault diagnosability is proposed.Then the influence of uncertainties on the evaluation results of fault diagnosability for affine nonlinear systems with multiple faults occurring simultaneously is analyzed.The numerical simulation results are obtained to show the effectiveness of the proposed evaluation scheme of fault diagnosability.

Surgical method choice and coincidence rate of pathological diagnoses in transduodenal ampullectomy: A retrospective case series study and review of the literature

BACKGROUND Transduodenal ampullectomy(TDA) is not in wide clinical use due to its low radical effect and a high recurrence rate of tumors. However,TDA is still an effective treatment method; it has great clinical value in cases of duodenal benign tumors,precancerous lesions,and benign and malignant borderline tumors,and can avoid the risks associated with pancreaticoduodenectomy with larger resection range and greater thoroughness than endoscopic papillectomy.AIM To investigate the surgical method choice and the coincidence rate of pathological diagnoses in TDA for ampullary neoplasms.METHODS Ten patients with ampullary neoplasms underwent TDA based on the fact that their endoscopic biopsy results suggested benign lesions,and the endoscopic ultrasound(EUS)-assessed tumors were resectable. All cases underwent duodenal ampullary lesion endoscopic biopsy,intraoperative frozen-section pathological examination,and postoperative pathological examination.RESULTSThis study included seven patients with benign tumors and three with malignant tumors(1 pTis,2 pT1),according to the postoperative pathology results. The coincidence rate of the postoperative pathology results with the intraoperative frozen-section biopsy results was 100%(10/10),and the coincidence rate with the endoscopic biopsy results was 70%(7/10) based on pathological characteristics.The endoscopic biopsy false-negative rate was 30%(3/10). All patients were followed for 6 to 70 mo without tumor recurrence or metastasis.CONCLUSION The coincidence rate of postoperative pathology results,intraoperative frozensection pathology results,and endoscopic biopsy results is the restraining factor of TDA clinical application. Endoscopic biopsy results and EUS have importance relevance to surgical planning. Intraoperative frozen-section pathology results have a significant influence on the choice of surgical procedure.

Detecting Lumbar Implant and Diagnosing Scoliosis from Vietnamese X-Ray Imaging Using the Pre-Trained API Models and Transfer Learning

With the rapid growth of the autonomous system,deep learning has become integral parts to enumerate applications especially in the case of healthcare systems.Human body vertebrae are the longest and complex parts of the human body.There are numerous kinds of conditions such as scoliosis,vertebra degeneration,and vertebrate disc spacing that are related to the human body vertebrae or spine or backbone.Early detection of these problems is very important otherwise patients will suffer from a disease for a lifetime.In this proposed system,we developed an autonomous system that detects lumbar implants and diagnoses scoliosis from the modified Vietnamese x-ray imaging.We applied two different approaches including pre-trained APIs and transfer learning with their pre-trained models due to the unavailability of sufficient x-ray medical imaging.The results show that transfer learning is suitable for the modified Vietnamese x-ray imaging data as compared to the pre-trained API models.Moreover,we also explored and analyzed four transfer learning models and two pre-trained API models with our datasets in terms of accuracy,sensitivity,and specificity.

Conditional Diagnosability of the Locally Twisted Cubes under the PMC Model

In a multiprocessor systems, it is important to local and to replace the faulty processors to maintain systempsilas high reliability. The fault diagnosis, which is the process of identifying fault processors in a multiprocessor system through testing. The conditional diagnosis requires that for each processor u in a system, all the processors that are directly connected to u do not fail at the same time. In this paper, we study the conditional diagnosability of the n-dimensional locally twisted cubes. After showing some properties of the locally twisted cubes, we prove that it under the PMC model is 4n – 7 for n ≥ 5.

Analysis of superheater's pipe wall overtemperature by fault tree diagnose

After research on a 2000t/h subcritical forced-circulation balanced ventilation were applied boiler and the structure and operation of its auxiliary system builds up this heat transfer model of a superheater's pipe wall and analyze the effect of primary factors on the overtemperature of the pipe wall. Fault tree structure was used to uncover the multiplayer logic between the overtemperature of the superheater's pipe wall and the faults.

Character Diagnosis for Surface-Wave Plasmas Excited by Surface Plasmon Polaritons

The plasma parameters of planar-type surface-wave plasmas （SWPs） are diagnosed based on the resonant excitation of surface plasmon polaritons （SPPs）. The plasma parameter distributions are obtained by changing the discharge conditions of gas pressure and incident power. The measured experimental results show that the plasma near the heating layer is excited by surface waves of SPPs while the plasma located downstream originates from diffusion Moreover, the influence of high-frequency oscillations plays a significant role in producing the proposed SWPs with bi-Maxwellian electron energy distributions.

The Tightly Super 3-Extra Connectivity and Diagnosability of Locally Twisted Cubes

Diagnosability of a multiprocessor system G is one important measure of the reliability of interconnection networks. In 2016, Zhang et al. proposed the g-extra diagnosability of G, which restrains that every component of G – S has at least (g + 1) vertices. The locally twisted cube LTQn is applied widely. In this paper, we show that LTQn is tightly (4n – 9) super 3-extra connected for n ≥ 6 and the 3-extra diagnosability of LTQn under the PMC model and MM* model is 4n - 6 for n ≥ 5 and n ≥ 7, respectively.