Types Of Fingerprints Research Articles

Organic fluorophores in developing latent fingerprints: an up-to-date review

AbstractFingerprints have been established as legitimate and critical forensic science evidence for identifying criminals beginning as early as the twentieth century. This article details the different types of fingerprints, the broad range of surfaces, and various development techniques available for on-site latent fingerprint collection, with fingerprint fidelity being of prime significance. Fluorescent imaging of latent finger-marks benefits from enhanced sensitivity, selectivity, and reduced reliance on instruments. The review focuses on the topical developments reported on small-molecule organic fluorophores which could be used to develop latent prints through powder, solution, and fuming approaches. These inexpensive luminophores extend wide emission profiles that span the visible spectrum to visualize the latent prints with exceptional resolution, free from any interference originating from the background surface. Further, the critical challenges and prospective scope for future research developments to improve the detection of latent fingerprints using small-molecule fluorophores are also discussed. Graphical abstract

Trace Measurement by Fingerprint Type Surface Tactile Sensor for Texture Detection

Trace Measurement by Fingerprint Type Surface Tactile Sensor for Texture Detection

DACTYLOGRAPHY: The Scientific Study of Fingerprint

Fingerprint analysis has become important because of its reliability and scientific explanation in the courts. Fingerprint has three principles classification, uniqueness and identification. Now there are digital biometric scanner has the ability to access by fingerprint within a minute. This Automated Fingerprint Identification System has made easier to accessible. But the reliability would be little less than other ways to identify fingerprints. Because due to the malfunction of the scanner database can be manipulated or hacked by anyone to access the security. There are various types of fingerprints which needed to know for analyzing it. These types are 1. Patent Fingerprint(visible) 2. Latent Fingerprint(invisible) 3. Plastic/3d; to developing fingerprints there are different methods for different types of fingerprint. Fingerprint experts tend to develop fingerprint in crime scene for solving the crime. Whereas fingerprint become most important physical evidence in matter of solving crime. For developing to analyzing fingerprint expertise would be necessary for establishment. But there are various challenges for forensic experts which are distort prints, smudge prints, contaminated crime scene, database error, partial prints, etc. for the recording of the fingerprint on the wall also be challenging for experts which only can be recorded by photography. Henry classification system would be time consuming to evaluate the recorded fingerprints. Then there is chemical method to develop fingerprint due to the secretion of sweat, oil and other body fluids which make an invisible print. The outermost skin layers have various pores through where body fluid tend to secrets which is responsible for making fingerprints. To make it visible various chemical need to apply so that the secreted body fluid will react with chemical and its visibility will appear. There are some pattern types which tend to administer the individuality such as Arch, Loop and Whorl. Every pattern in itself is unique which can’t be match with any other person. Another way is ridge characteristics which is also valuable way to identify fingerprint. Because of its unique characteristics which tend to differ for every person and not only one characteristics but also 14 ridge characteristics are possible to identify through fingerprint. Porescopy and Edgescopy these are other ways to identify fingerprints but it is not practicable in India right now. There are basic two ways to develop latent fingerprints powder method and chemical methods. Through powder method, various powder and brushes will be need to develop fingerprints but it need to be know the surface where fingerprints are found. In chemical method there would be various chemical compound which needed to apply for the development of the latent fingerprints

Multimodal Biometric Analysis: Exploring Lip Print, Fingerprint, and Blood Group Correlations in the Firozabad Populations

Abstract Aim: This study aimed to establish a relationship between blood groups, fingerprint types, and lip prints that can be utilized to distinguish between genders when identifying a person. Materials and Methods: The study was conducted among 200 female and male participants ages 28 of Firozabad-based populations. Along with the blood types, finger, and lip print patterns were also gathered. For statistical analysis, SPSS Normal H. Nie, standford, california, united states was employed. The qualitative factors’ frequency and percentages were determined. Results: In the present study, overall Type II lip print in males and Type III lip print in females, loop fingerprint pattern and blood group O(+)ve in both sexes were most prevalent, and our findings were matched with the study of Harsha and Jayaraj, in which Type II (39.9%), loops 27 (42.2%), and blood group O(+)ve were observed predominantly. Conclusion: Personal identification of an individual can be greatly aided by personal characteristics such as blood group type, fingerprints, and lip prints. The most frequent patterns in our study were the Type II lip pattern in the males and Type III lip pattern in females, blood group O+, and the loop fingerprint pattern.

Assessment of lip and finger print patterns in patients with type 2 diabetes mellitus and dental caries: A cross-sectional study

Abstract Background and Objectives: Globally, the prevalence of diabetes and dental caries has been soaring high in recent times. There is a constant effort in the scientific community to develop a reliable and economic early predictor which can serve the purpose of mass screening of genetically vulnerable populations. The present study attempts to evaluate the different types of lip prints and finger prints in diabetes mellitus and dental caries and to see association between the most common diseases (diabetes mellitus and dental caries) if any. Materials and Methods: Study subjects included 100 subjects (50 uncontrolled Type II diabetes mellitus patients and 50 healthy controls) in the age group of 30–80 years among the population of Lucknow. Lip prints were obtained using lipstick and cellophane paper, analysed, and classified using Suzuki and Tsuchihashi’s classification. Finger prints were obtained using inkpad, analysed, and classified using Henry’s system of classification. Results: We found loop-type fingerprints and type IV lip prints associated with diabetic patients. Non-diabetics showed loop-type fingerprints and type I lip prints. We found increased dental caries incidence (DMFT scores) in diabetics. Diabetic subjects with caries showed loop fingerprints, which reiterated our earlier findings but are not associated with type IV lip prints. Non-diabetics with caries showed arch fingerprints, but they are not associated with type I lip prints. We found that DMFT scores that we used to assess caries did not associate well with lip prints. Conclusion: The results from our study strongly suggest that dermatoglyphics can be used as a non-invasive technique to mass screen for diabetes as well as dental caries as both diseases are predominantly associated with loop-type fingerprints. Type IV lip prints could be used to screen for diabetes, but no association of lip prints was seen in patients with caries.

Unraveling the Enigma of Dental Caries through Conventional Analysis using Finger Print Forensics among the Students of Nadiad City, Gujarat

Introduction: There is growing interest in identifying genetic predispositions to dental caries using non-invasive tools. Dermatoglyphics, examining dermal ridge patterns on the hands and feet, is linked to genetic foundations. Galton’s theory asserts fingerprint constancy. In dentistry, dermatoglyphics gains attention for its correlation with students pathologies. This study evaluates the association of dental caries in students with dermatoglyphics. Materials and Methods: The research at the Department of Oral and Maxillofacial Pathology, Dharmsinh Desai University, involved 50 subjects (28 females and 22 males) with normal fingerprints. Ethical clearance was obtained from the institute’s Ethics Committee. Fingerprints were recorded using the conventional method, employing ink, paper, roller, glass slab and sponging pad. The prints were examined, classified and analysed through the Cummins method, categorising them into whorls, loops and arches. The dental caries were assessed through the decayed, missing and filled teeth (DMFT) scores of the participants. The DMFT score was tested for association and difference according to the type of fingerprint pattern. Results: Females commonly exhibit loop patterns, while males show an equal distribution between loops and whorls. The connection between fingerprint patterns and DMFT scores is notable. A Chi-square test for left-side patterns (Chi-square = 27.74, P = 0.002) reveals a strong association, linking specific patterns to distinct DMFT scores. Similarly, for right-side patterns (Chi-square = 11.349, P = 0.045), there is significance, emphasising the relationship between fingerprint types and dental caries. Conclusion: The study examined the connection between fingerprint patterns and dental caries in children, discovering a positive correlation. Specific patterns such as arch, loop and whorl were linked to caries presence or absence, suggesting their potential as an early detection tool, although accuracy might vary based on genetic factors.

Ensemble multiclassification model for predicting developmental toxicity in zebrafish

In recent years, with the rapid development of society, organic compounds have been released into aquatic environments in various forms, posing a significant threat to the survival of aquatic organisms. The assessment of developmental toxicity is an important part of environmental safety risk systems, helping to identify the potential impacts of organic compounds on the embryonic development of aquatic organisms and enabling early detection and warning of potential ecological risks. Additionally, binary classification models cannot accurately classify organic compounds. Therefore, it is crucial to construct a multiclassification model for predicting the developmental toxicity of organic compounds. In this study, binary and multiclassification models were developed based on the ToxCast™ Phase I chemical library and literature data. The random forest, support vector machine, extreme gradient boosting, adaptive gradient boosting, and C5.0 decision tree algorithms, as well as 8 types of molecular fingerprint were used to establish a multiclassification base model for predicting developmental toxicity through 5-fold cross-validation and external validation. Ultimately, a multiclassification ensemble model was derived through a voting method. The performance of the binary ensemble model, as measured by the balanced accuracy, was 0.918, while that of the multiclassification model was 0.819. The developmental toxicity voting ensemble model (DT-VEM) achieved accuracies of 0.804, 0.834, and 0.855. Furthermore, by utilizing the XGBoost machine learning algorithm to construct separate models for molecular descriptors and substructure molecular fingerprints, we identified several substructures and physical properties related to developmental toxicity. Our research contributes to a more detailed classification of developmental toxicity, providing a new and valuable tool for predicting the developmental toxicity effects of unknown compounds. This supplement addresses the limitations of previous tools, as it offers an enhanced ability to predict potential developmental toxicity in novel compounds.

Fingerprints as an Index for Cooperation in Children during Dental Treatment: A Cross-sectional Study

Abstract Background: The behaviour of a child is an essential factor in paediatric dentistry. Fingerprints are unique and constant over a lifetime. Some studies indicate a relationship between fingerprint type and behaviour mode of children during dental treatment. Therefore, fingerprint patterns might help to predict the extent of cooperation by children during dental procedures. Aim: The aim of the study was to investigate the possible relationship between fingerprint patterns and the cooperation of children during dental procedures. Materials and Methods: In this cross-sectional study, 80 children aged 4–7 years were examined. The children meeting the inclusion criteria in their first dental visit are scheduled for a dental procedure. During this procedure, based on Frankl’s scale, the child’s behaviour is assessed. Subjects were categorised as cooperative or uncooperative. A different examiner records the child’s fingerprint. The data were compared with SPSS Version 21. Intergroup comparison was made using the Chi-square test at a significance level of P < 0.05. Results: Amongst the 47 cooperative children, 34 children had loop fingerprint pattern, 7 had whorl fingerprint pattern and 6 had arch fingerprint patterns. Amongst the 33 uncooperative children, 14 had loop fingerprint pattern, 15 had whorl fingerprint pattern and 4 had arch fingerprint pattern. The difference was significant between the groups (P < 0.01) based on dermatoglyphic characteristics, with the loop pattern being seen in cooperative children and the whorl pattern in uncooperative children. Conclusion: There is a relationship between fingerprint type and behaviour mode of children, with loop pattern being the primary fingerprint type amongst the cooperative group and whorl pattern amongst the uncooperative group.

Determination and Correlation of Finger Print Pattern and Blood Grouping in Diabetes Mellitus: An Analytical Study

BACKGROUND: To Determine and correlate between fingerprint patterns and blood group in type II diabetes mellitus. RESULTS: One hundred individuals with type II diabetes mellitus and One hundred normal individuals as controls were selected for study. Fingerprints were obtained by dactyloscopy. Various fingerprint patterns like arches, whorls, loops were studied and correlated with the respective blood group based on ABO system among the volunteer controls and diabetics. After obtaining results, the analysis was made using Chi square test and One way ANOVA which showed, the most predominant fingerprint pattern among the 100 diabetic volunteers is “Arch pattern” and the most predominant blood group was “O” positive among the diabetic volunteers. Also correlating both finger print and blood group “Whorl pattern” with “B” blood group showed higher predominance among the healthy volunteers. CONCLUSIONS: This is the first study correlating finger print pattern with blood grouping in subjects of Diabetes mellitus. Our study findings showed that O positive blood group with arch pattern type of finger print was the most common findings among diabetes. Thus correlating fingerprint pattern and blood grouping act as a early predictor tool for diagnosing Type II Diabetes mellitus.

Machine learning accelerates pharmacophore-based virtual screening of MAO inhibitors

Nowadays, an efficient and robust virtual screening procedure is crucial in the drug discovery process, especially when performed on large and chemically diverse databases. Virtual screening methods, like molecular docking and classic QSAR models, are limited in their ability to handle vast numbers of compounds and to learn from scarce data, respectively. In this study, we introduce a universal methodology that uses a machine learning-based approach to predict docking scores without the need for time-consuming molecular docking procedures. The developed protocol yielded 1000 times faster binding energy predictions than classical docking-based screening. The proposed predictive model learns from docking results, allowing users to choose their preferred docking software without relying on insufficient and incoherent experimental activity data. The methodology described employs multiple types of molecular fingerprints and descriptors to construct an ensemble model that further reduces prediction errors and is capable of delivering highly precise docking score values for monoamine oxidase ligands, enabling faster identification of promising compounds. An extensive pharmacophore-constrained screening of the ZINC database resulted in a selection of 24 compounds that were synthesized and evaluated for their biological activity. A preliminary screen discovered weak inhibitors of MAO-A with a percentage efficiency index close to a known drug at the lowest tested concentration. The approach presented here can be successfully applied to other biological targets as target-specific knowledge is not incorporated at the screening phase.

Molecular characterization of ESBL-producing uropathogenic Escherichia coli isolates among kidney transplant patients: Emergence and spread of B2-ST131 clone type

This study was conducted to identify the distribution of virulence determinants in uropathogenic Escherichia coli (UPEC) isolates obtained from kidney transplant (KTP) and non-transplant patients (non-KTP) with urinary tract infections (UTI). Additionally, the (GTG)5 fingerprinting technique was used to investigate the genetic diversity of Extended-Spectrum B-Lactamase (ESBL)-positive isolates.In this case-control study, 111 urine isolates were obtained from non-KTPs and KTPs, respectively. The presence of genetic markers encoding adhesion proteins, toxins and major E. coli phylogroups was assessed through PCR amplification. Molecular typing of ESBL-positive UPEC strains was performed using (GTG)5 fingerprinting and Multilocus sequence typing (MLST) techniques.Overall, 65 and 46 UPEC isolates were obtained from non-KTPs and KTPs, respectively. Among the studied isolates, traT (85.6%) gene was the most frequently observed virulence gene, followed by kpsMT (49.5%). Using the 80% cut-off point, all the 35 UPEC isolates were classified into four major clusters, namely A, B, C, and D. The majority of the Sequence Type (ST) 131 isolates belonged to cluster A. Additionally, three ST1193 isolates belonged to cluster A and phylogroup B2. Moreover, ST38, ST131 and ST10 were in different cluster.In general, we observed significant differences in the papA, ompT, sat, and vat genes between KTPs and non-KTPs. Furthermore, since all the isolates carried one or more virulence factors (VFs), these findings are concerning in the context of managing UTIs caused by the UPEC strain. Additionally, the distribution of ST and Clonal Complex (CC) among isolates in the main clusters revealed significant differences between MLST and (GTG)5 fingerprinting analysis.

Noteworthy Consensus Effects of D/E Residues in Umami Peptides Used for Designing the Novel Umami Peptides.

Aspartic acid (D) and glutamic acid (E) play vital roles in the umami peptides. To understand their exact mechanism of action, umami peptides were collected and cut into 1/2/3/4 fragments. Connecting D/E to the N/C-termini of the fragments formed D/E consensus effect groups (DEEGs), and all fragments containing DEEG were summarized according to the ratio and ranking obtained in the above four situations. The interaction patterns between peptides in DEEG and T1R1/T1R3-VFD were compared by statistical analysis and molecular docking, and the most conservative contacts were found to be HdB_277_ARG and HdB_148_SER. The molecular docking score of the effector peptides significantly dropped compared to that of their original peptides (-1.076 ± 0.658 kcal/mol, p value < 0.05). Six types of consensus fingerprints were set according to the Top7 contacts. The exponential of relative umami was linearly correlated with ΔGbind (R2 = 0.961). Under the D/E consensus effect, the electrostatic effect of the umami peptide was improved, and the energy gap between the highest occupied molecular orbital-the least unoccupied molecular orbital (HOMO-LUMO) was decreased. The shortest path map showed that the peptides had similar T1R1-T1R3 recognition pathways. This study helps to reveal umami perception rules and provides support for the efficient screening of umami peptides based on the material richness in D/E sequences.

Artificial Intelligence and Machine Learning Methods to Evaluate Cardiotoxicity following the Adverse Outcome Pathway Frameworks.

Cardiovascular disease is a leading global cause of mortality. The potential cardiotoxic effects of chemicals from different classes, such as environmental contaminants, pesticides, and drugs can significantly contribute to effects on health. The same chemical can induce cardiotoxicity in different ways, following various Adverse Outcome Pathways (AOPs). In addition, the potential synergistic effects between chemicals further complicate the issue. In silico methods have become essential for tackling the problem from different perspectives, reducing the need for traditional in vivo testing, and saving valuable resources in terms of time and money. Artificial intelligence (AI) and machine learning (ML) are among today's advanced approaches for evaluating chemical hazards. They can serve, for instance, as a first-tier component of Integrated Approaches to Testing and Assessment (IATA). This study employed ML and AI to assess interactions between chemicals and specific biological targets within the AOP networks for cardiotoxicity, starting with molecular initiating events (MIEs) and progressing through key events (KEs). We explored methods to encode chemical information in a suitable way for ML and AI. We started with commonly used approaches in Quantitative Structure-Activity Relationship (QSAR) methods, such as molecular descriptors and different types of fingerprint. We then increased the complexity of encoders, incorporating graph-based methods, auto-encoders, and character embeddings employed in neural language processing. We also developed a multimodal neural network architecture, capable of considering the complementary nature of different chemical representations simultaneously. The potential of this approach, compared to more conventional architectures designed to handle a single encoder, becomes apparent when the amount of data increases.

Novel machine learning approach toward classification model of HIV-1 integrase inhibitors.

HIV-1 (human immunodeficiency virus-1) has been causing severe pandemics by attacking the immune system of its host. Left untreated, it can lead to AIDS (acquired immunodeficiency syndrome), where death is inevitable due to opportunistic diseases. Therefore, discovering new antiviral drugs against HIV-1 is crucial. This study aimed to explore a novel machine learning approach to classify compounds that inhibit HIV-1 integrase and screen the dataset of repurposing compounds. The present study had two main stages: selecting the best type of fingerprint or molecular descriptor using the Wilcoxon signed-rank test and building a computational model based on machine learning. In the first stage, we calculated 16 different types of fingerprint or molecular descriptors from the dataset and used each of them as input features for 10 machine-learning models, which were evaluated through cross-validation. Then, a meta-analysis was performed with the Wilcoxon signed-rank test to select the optimal fingerprint or molecular descriptor types. In the second stage, we constructed a model based on the optimal fingerprint or molecular descriptor type. This data followed the machine learning procedure, including data preprocessing, outlier handling, normalization, feature selection, model selection, external validation, and model optimization. In the end, an XGBoost model and RDK7 fingerprint were identified as the most suitable. The model achieved promising results, with an average precision of 0.928 ± 0.027 and an F1-score of 0.848 ± 0.041 in cross-validation. The model achieved an average precision of 0.921 and an F1-score of 0.889 in external validation. Molecular docking was performed and validated by redocking for docking power and retrospective control for screening power, with the AUC metrics being 0.876 and the threshold being identified at -9.71 kcal mol-1. Finally, 44 compounds from DrugBank repurposing data were selected from the QSAR model, then three candidates were identified as potential compounds from molecular docking, and PSI-697 was detected as the most promising molecule, with in vitro experiment being not performed (docking score: -17.14 kcal mol-1, HIV integrase inhibitory probability: 69.81%).

ZJUT-EIFD: A Synchronously Collected External and Internal Fingerprint Database.

External fingerprints (EFs) based only on epidermal information are vulnerable to spoofing attacks and non-ideal skin conditions. To solve such shortcomings, internal fingerprints (IFs) collected using optical coherence tomography (OCT) have been proposed and widely researched. However, the development of IF is limited by the lack of in-depth researches on the IF and the EF-IF interoperability, which is partially caused by the lack of public OCT database. The obvious gap in the applications of EF and IF recognition motivated us to design and publish a comprehensive fingerprint database containing both traditional EFs and OCT IFs, denoted as ZJUT-EIFD. To the best of our knowledge, ZJUT-EIFD is the first public database that combines OCT and total internal reflection (TIR) via synchronous acquisition, with 399 different fingers from 60 subjects. In this article, the composition of the database, the quality of EFs and IFs, and the verification performance of different types of fingerprints were detailed. In addition, potential application directions of ZJUT-EIFD were demonstrated. ZJUT-EIFD can serve benchmarks and interoperability tests for EF-IF research, which will promote the research and development of EF and IF.

Dermatoglyphics: The elusive nexus decoding the conundrum of amlodipine-induced gingival enlargement – A cross-sectional, pilot, observational study

Abstract: Background: Dermatoglyphics is a scientific study dealing with the dermal ridge configurations of the palmar and plantar surfaces of the upper and lower limbs. Drug-induced gingival enlargement refers to engorgements in gingiva as a consequence of drug usage. This pilot study was done to see the pattern of fingerprints using dermatoglyphics and amlodipine-induced gingival enlargement in hypertensive patients. Materials and Methods: The subjects for the study were prospectively recruited from the outpatients reporting to the Department of Periodontology, Tamil Nadu Government Dental College during the study period of 3 months following the inclusion criteria. Twenty people presenting with amlodipine induced gingival enlargement were thus recruited for the study and their fingerprint patterns were recorded and analyzed with the assistance of magnifying lens. Results: It was thus observed that there was a preponderance of whorls (55%) while observing fingerprint patterns of the recipients of amlodipine presenting with gingival enlargement. Females outnumbered males in the study. The gender difference was seen in the type of fingerprint. A preponderance of whorls (55%, 11 participants) 30% arch (6 participants) and 15% with loop (3 participants). Similar results were recorded in both genders with whorl recording the highest incidence of 80% and 46.67% in males and females, respectively. No zero loop patterns were recorded in males. Conclusion: The relative preponderance of whorls being observed in this study might be a predictive marker for those amlodipine consumers with a heightened predilection to develop gingival enlargement The subdued expression of loops which are otherwise the most commonly expressed fingerprint pattern among the humans, and the ascendancy of whorls characterizing this category of people is indeed a finding, which by further supportive studies, could help find preventive strategies to combat amlodipine-induced gingival enlargement.

Implementing multi-factor authentication (MFA) for robust network access security

In a world that is becoming more and more digital, Multi-Factor Authentication (MFA) is essential for making network access safer. The purpose of this study is to suggest a way to adopt MFA that will make network systems more resistant to cyber risks and illegal access. Adding different types of authentication, like passwords, fingerprints, smart cards, and one-time codes, is the first part of the suggested approach. By making users prove their identity in more than one way, the system adds more layers of protection, making it less likely that someone will get in without permission, even if one of the factors is broken. In addition, the application stresses how important it is to have a strong method for verifying identities. This includes making sure users are who they say they are by using personal data, like fingerprints or face recognition, which are hard to fake or copy. The system also has flexible authentication features that change the amount of authentication needed based on things like the user’s position, the device they’re using, and how they usually behave. This flexible method improves the user experience by reducing the hassle of identification while still meeting high security standards. Furthermore, the application includes full logs and tracking tools to quickly find and stop any shady actions. By constantly watching tries at authentication and how users act, the system can spot possible security holes and take steps to reduce risks.

Assessing the Relationship between Fingerprints and Lip Prints Patterns with Gender and Blood Group at a Tertiary Care Hospital

Background: Fingerprints and lip prints are unique biometric markers extensively studied in forensic science for individual identification. Objective: This study investigates the relationship between fingerprints and lip print patterns concerning gender and blood group characteristics within a tertiary care hospital setting. Study Design: Cross-sectional study. Settings: Department of Forensic Medicine Loralai Medical College, Loralai Pakistan. Duration: From August 2022 to January 2023. Methods: Total 150 patients involving the collection of fingerprints and lip prints from participants were enrolled. Gender and blood group information were also recorded. Statistical analysis, including correlation tests and chi-square analysis, was employed to examine associations between biometric patterns and demographic variables. Results: The mean age of the sample population was 37.53 years, with a standard deviation of 3.2 years. Approximately 53.33% of the participants were male, while 46.67% were female. Groups A and O each comprised 26.67% of the sample, while group AB accounted for 16.67%. Type II and Type III patterns followed, accounting for 23.33% and 20.00%, respectively, while Type I patterns were least common at 16.67%. In terms of fingerprint types, loops were the most prevalent at 46.67%, followed by whorls at 30.00% and arches at 23.33%. The correlation indicates significant associations between lip prints and fingerprints with gender (χ² = 7.128, df = 3, p &lt; 0.001*) and between lip prints and fingerprints with blood group (χ² = 5.454, df = 3, p = 0.001*). These findings suggest that gender and blood group may influence the distribution of lip prints and fingerprints. Conclusion: This study demonstrates significant associations between fingerprints, lip prints, gender, and blood group characteristics among individuals at a tertiary care hospital. These findings underscore the potential utility of integrating biometric parameters for enhanced forensic identification and medical diagnostics.

Disentangling different levels of GAN fingerprints for task-specific forensics

Image generation using generative adversarial networks (GANs) has raised new security challenges recently. One promising forensic solution is verifying whether or not a suspicious image contains a GAN fingerprint, a unique trace left behind by the source GAN. Previous methods mainly focused on GAN fingerprint extraction while underestimating the downstream forensic applications, and the fingerprints are often single-level which only supports one specific forensic task. In this study, we investigate the problem of disentangling different levels of GAN fingerprints to satisfy the need for varying forensics tasks. Based on an analysis of fingerprint dependency revealing the existence of two levels of fingerprints in different signal domains, we proposed a decoupling representation framework to separate and extract two types of GAN fingerprints from different domains. An adversarial data augmentation strategy plus a transformation-invariant loss is added to the framework to enhance the robustness of fingerprints to image perturbations. Then we elaborated on three typical forensics tasks and the task-specific fingerprinting using different GAN fingerprints. Extensive experiments have verified our dependency analysis, the effectiveness and robustness of the proposed fingerprint extraction framework, and the applicability of task-specific fingerprinting in real-world and simulated scenarios.

Epidemiologic characteristics and drug resistance of isolated from blood culture escherichia coli in a hospital in Qinghai Province from 2016 to 2022

Objective: To explore the drug resistance of Isolated From Blood Culture Escherichia coli (E. coli) in a hospital in Qinghai over the past seven years, to evaluate the ability of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to analyze the homologous origin of E. coli, and to establish a protein fingerprint library to match with it, adjuvant clinical experience medication so as to provide the basis for the prevention and control of hospital-acquired infections. Methods: Retrospective analysis of blood cultures sent to hospitals from January 2016 to December 2022. Drug resistance and resistance changes in E. coli.A total of 1 841 E. coli strains were isolated from Qinghai Provincial People's Hospital from January 2016 to December 2022; all strains were identified by MALDI-TOF MS, and the VITEK2.0 drug sensitivity analyzer was applied for drug sensitivity analysis of the strains, and the mass spectrometry homology analysis and self-constructed protein fingerprint library were carried out by MALDI-Biotyper software; the protein fingerprint library was built by using WHONET5.6 software was used to statistically analyze the drug sensitivity results, SPSS23.0 software was used to analyze the relationship between fingerprint typing and drug sensitivity, and the χ2 test was used for intergroup comparisons. Results: A total of 1 841 strains of E. coli were detected in 4 582 positive blood culture specimens from January 2016 to December 2022, with a detection rate of 40.17%; the resistance rate of E. coli from blood sources to piperacillin/tazobactam and ceftriaxone was on the rise, and it was slightly decreased to cefepime, amikacin, levofloxacin, and sulfamethoxazole, and there was not much change to the rest of the drugs; After MALDI-Biotyper clustering analysis, the 1841 E. coli strains from Isolated From Blood Culture were classified into two major clusters and five subtypes, of which type Ⅰa1 accounted for about 40%, type Ⅰa2 accounted for about 2.7%, type Ⅰb accounted for about 3.8, type Ⅱa accounted for about 46%, and type Ⅱb accounted for about 7.5%. The detection rate of type Ⅰa1 E. coli was higher in general surgery (50.45%) and emergency surgery (50.92%), and the detection rate of type Ⅰb E. coli was higher in emergency medicine(10.05%)than in other departments. The drug sensitivity results of different subtypes were compared with each other, the resistance rate of type Ⅰa1 E. coli to cefepime was 21.3% higher than that of the remaining four types, and the difference was statistically significant (χ2=37.74,P=0.000); the resistance rate of type Ⅱ E. coli(>60%) to sulfamethoxazole was higher than that of type Ⅰ (<60%) as a whole, and the difference was statistically significant (χ2=15.248,P=0.004); and a preliminary database of homologous protein fingerprints of E. coli has been established E. coli homologous protein fingerprint library and validated. The drug susceptibility results of 1 288 E. coli strains in the validation set were statistically analyzed and compared with those in the training set. There was no significant difference(P>0.05). Conclusion: In recent years, the resistance rate of E. coli isolated from a hospital in Qinghai province to piperacillin/Tazobactam, cefepime, amicacin and other antibiotics has changed greatly. A fingerprint database of E. coli homologous protein was established, and it was found that the drug sensitivity data of E. coli were different among different fingerprint types. According to drug sensitivity, drug use could assist clinical experience and provide evidence for prevention and control of hospital illness.