Standard Database Research Articles

TCMSSD: A comprehensive database focused on syndrome standardization

BackgroudQuantitative and standardized research on syndrome differentiation has always been at the forefront of modernizing Traditional Chinese Medicine (TCM) theory. However, the majority of existing databases primarily concentrate on the network pharmacology of herbal prescriptions, and there are limited databases specifically dedicated to TCM syndrome differentiation. PurposeIn response to this gap, we have developed the Traditional Chinese Medical Syndrome Standardization Database (TCMSSD, http://tcmssd.ratcm.cn). MethodsTCMSSD is a comprehensive database that gathers data from various sources, including TCM literature such as TCM Syndrome Studies (Zhong Yi Zheng Hou Xue) and TCM Internal Medicine (Zhong Yi Nei Ke Xue) and various public databases such as TCMID and ETCM. In our study, we employ a deep learning approach to construct the knowledge graph and utilize the BM25 algorithm for syndrome prediction. ResultsThe TCMSSD integrates the essence of TCM with the modern medical system, providing a comprehensive collection of information related to TCM. It includes 624 syndromes, 133,518 prescriptions, 8,073 diseases (including 1,843 TCM-specific diseases), 8,259 Chinese herbal medicines, 43,413 ingredients, 17,602 targets, and 8,182 drugs. By analyzing input data and comparing it with the patterns and characteristics recorded in the database, the syndrome prediction tool generates predictions based on established correlations and patterns. ConclusionThe TCMSSD fills the gap in existing databases by providing a comprehensive resource for quantitative and standardized research on TCM syndrome differentiation and laid the foundation for research on the biological basis of syndromes.

Bio-pesticidal, Antimicrobial, and Anti-inflammatory Potentials of n-Hexane Fraction of Zanthoxylum armatum DC and its Chemical Profiling

Zanthoxylum armatum DC, commonly known as toothache tree, is utilized for treating inflamed gums. The plant’s volatile constituent possesses a robust fragrance and contributes to its tangy taste. This study investigates the bioactivities, including bio-pesticidal, antimicrobial, and anti-inflammatory properties as well as the chemical profiling of the n-hexane fraction based on GC-MS analysis. The evaluated activities involve contact toxicity, microplate alamar blue assay, against three different insects, five bacteria, and seven fungi, and oxidative burst assay. The NIST library serves as a standard reference database for constituent identification. Remarkable insecticidal activities comparable to the standard drug permethrin were observed, particularly against Rhyzopertha dominica (100%), Tribolium castaneum (60%), and Sitophilus oryzae (50%). The fraction exhibited significant antifungal activity against Fusarium lini (85%) and notable inhibition against B. subtillis (67.27%) and S. aureus (65.25%). Potent anti-inflammatory effects were noted with an IC50 value of 11.2±1.9 μg/ml, equivalent to standard ibuprofen at various concentrations. GC-MS analysis identified twenty compounds, with major ones including trans-13-octadecenoic acid (36.08%), cis-9 hexadecenoic acid (18.66%), and 2-propenoic acid 3 phenyl methyl ester (11.08%). The diverse bioactivities observed may be attributed to the varied nature of compounds such as polyunsaturated fatty acids and their oxides. This research revealed the potential of Z. armatum as a potential bio-pesticide, anti-inflammatory, and antimicrobial agent.

Pharmacological Topical Therapy for Intra-Oral Post Traumatic Trigeminal Neuropathic Pain: A Comprehensive Review.

Background: The efficacy of topical treatments in alleviating neuropathic pain is well-established. However, there is a paucity of research on topical interventions designed specifically for intra-oral application, where the tissue composition differs from that of exposed skin. Methods: This comprehensive review endeavors to assess the extant evidence regarding the efficacy of topical treatments in addressing neuropathic pain within the oral cavity. Utilizing combinations of search terms, we conducted a thorough search across standard electronic bibliographic databases-MEDLINE (via PubMed), Embase, Google Scholar, and Up to Date. The variables under scrutiny encompassed topical treatment, local intervention, chronic oral and orofacial pain, and neuropathic pain. All pertinent studies published in the English language between 1992 and 2022 were included in our analysis. Results: Fourteen relevant manuscripts were identified, primarily consisting of expert opinions and case reports. The comprehensive review suggests that topical treatments, especially when applied under a stent, could be effective in mitigating neuropathic pain in the oral area. However, it is crucial to conduct further studies to confirm these preliminary results. The limitations of the reviewed studies, mainly the reliance on expert opinions, small sample sizes, inconsistent study designs, and a lack of long-term follow-up data, highlight the need for more rigorous research. Conclusions: Although initial findings indicate topical treatments may be effective for oral neuropathic pain, the limitations of current studies call for more thorough research. Further comprehensive studies are essential to validate the efficacy of these treatments, standardize procedures, and determine long-term results. This will provide clearer guidance for treating chronic neuropathic pain in the oral cavity.

Influence of light neutron-rich exotic nuclei in deciding abundance patterns relevant for explosive nucleosynthesis

Exotic neutron-rich nuclei play an important role in r-process nucleosynthesis. We study the effect of including proper nuclear structure to calculate neutron capture and photodisintegration rates, especially for 19C and 20N, under the aegis of post-form distorted wave Born approximation (FRDWBA). Considering a network of neutron-rich C–N–O isotopes, we show the effect on abundance calculations in a neutrino-driven wind environment of a core-collapse supernova at two critical temperatures T 9 = 3.4 and 0.62. The results are compared for nuclear physics inputs sourced from the FRDWBA, statistical estimates, and standard databases. At temperatures T 9 = 3.4, where alpha capture, or at T 9 = 0.62, where the neutron capture dominates, one can distinguish the effect of accounting for the exotic nature of the nuclei involved. Furthermore, we assess the impact of varying neutron capture and photodisintegration rates involving halo or bubble structures on the abundances within the network.

The Use of Artificial Intelligence (AI) is Customer Service Process in the Pharmaceutical Industry: A Case Study in Public Laboratories

The Consumer Service (SAC) is an important communication and relationship channel with the customer. This study sought automated data collection and analysis techniques to trace profiles and strategies that are part of the SAC 4.0 model in official laboratories A and B. The method consisted of document analysis of standardized procedures and database research using the keywords SAC, artificial intelligence, and pharmaceutical industry. The results showed that laboratories A and B use the SAC 2.0 model and do not apply AI techniques. Evidence shows that pharmaceutical industries in Brazil use the SAC 4.0 model. It is concluded that the AI tools in the SAC contribute to improvements and efficiency in the process.

Comparative Analysis of Phytocompound Variations in Leaves, Bark and Roots of Allspice (Pimenta Dioica) Collections in Tanzania

Allspice, scientifically known as Pimenta dioica, holds potential as a natural source of beneficial compounds that have been historically used to address various human health concerns. The aim of this research was to explore differences in the compounds found in parts of Allspice (i.e., the leaves, bark, and roots). Petroleum ether, dichloromethane, and methanol were used to extract the substances from each part; the resulting crude extracts were then analyzed using gas chromatography mass spectrometry. To interpret the obtained data, the National Institute of Standards and Technology database was referred to for a spectra analysis. The findings indicated that the leaves contained 81 phytocompounds, bark had 18 types, and roots exhibited 12 varieties. Prominent phytocompounds found in the leaves were eugenol in 72.24% – 73.91% of the total compounds detected. Bark was primarily composed of 1,4-Methanoazulene, decahydro-4,8,8-trimethyl-9-methylene-, [1S-(1. alpha.,3a. beta.,4. alpha.,8a. beta.)] in 74.35% – 84.24%; while roots contained γ-sitosterol at an 86.08% concentration level. In terms of solvent performance, methanol exhibited high efficiency on leaves, while dichloromethane demonstrated optimal results on bark and roots. The findings confirm significant variations in phytocompound composition in different parts of Allspice and underscores the importance of considering specific types of phytocompounds, as well as extraction techniques to achieve valuable outcomes.

An efficient multimodal sentiment analysis in social media using hybrid optimal multi-scale residual attention network

Sentiment analysis is a key component of many social media analysis projects. Additionally, prior research has concentrated on a single modality in particular, such as text descriptions for visual information. In contrast to standard image databases, social images frequently connect to one another, making sentiment analysis challenging. The majority of methods now in use consider different images individually, rendering them useless for interrelated images. We proposed a hybrid Arithmetic Optimization Algorithm- Hunger Games Search (AOA-HGS)-optimized Ensemble Multi-scale Residual Attention Network (EMRA-Net) technique in this paper to explore the modal correlations including texts, audio, social links, and video for more effective multimodal sentiment analysis. The hybrid AOA-HGS technique learns complementary and comprehensive features. The EMRA-Net uses two segments, including Ensemble Attention CNN (EA-CNN) and Three-scale Residual Attention Convolutional Neural Network (TRA-CNN), to analyze the multimodal sentiments. The loss of spatial domain image texture features can be reduced by adding the Wavelet transform to TRA-CNN. The feature-level fusion technique known as EA-CNN is used to combine visual, audio, and textual information. The proposed method performs significantly better than the existing multimodel sentimental analysis techniques of HALCB, HDF, and MMLatch when evaluated using the Multimodal Emotion Lines Dataset (MELD) and EmoryNLP datasets. Also, even though the size of the training set varies, the proposed method outperformed other techniques in terms of recall, accuracy, F score, and precision and takes less time to compute in both datasets.

Identification of environmental Actinobacteria in buildings by means of chemotaxonomy, 16S rRNA sequencing, and MALDI-TOF MS.

The manuscript addresses the challenges in identifying environmental bacteria using matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) Biotyper-based protein profiling. The matter of the studies-actinobacterial strains-has been isolated mostly from building materials that originated from a confirmed moisture-damaged situation. Polyphasic taxonomy, 16S RNA gene sequencing, and MALDI-TOF mass spectrometry were applied for identification purposes. In this experimental paper, a few important facts are highlighted. First, Actinobacteria are abundant in the natural as well as built environment, and their identification on the species and genus levels is difficult and time-consuming. Second, MALDI-TOF MS is an effective tool for identifying bacterial environmental strains, and in parallel, continuous enrichment of the proteomics mass spectral databases is necessary for proper identification. Third, the chemical approach aids in the taxonomical inquiry of Actinobacteria environmental strains.

Development and standardization of Indian Alcohol Photo Stimuli (IAPS) for cue-reactivity paradigms in patients with alcohol use disorder.

The availability of appropriate alcohol-related stimuli is a crucial concern for the evaluation and treatment of patients with alcohol dependence syndrome. The study aimed to standardize alcohol-related images with cultural relevance to the Indian setting. We produced an extensive database of 203 pictures, the Indian Alcohol Photo Stimuli (IAPS), portraying different categories and types of alcoholic beverages, after removing the confounding effects of low-level stimulus parameters (e.g. brightness and blurriness). Thirty patients with alcohol dependence syndrome, currently abstinent, rated each image on visual analog scale (VAS) ranging from 0 (no craving) to 10 (extreme), to determine how typical the stimuli served as craving-relevant stimuli. The mean VAS scores across beverages (ordered from highest to lowest) were whiskey >rum >beer >wine >vodka. Repeated-measures analysis of variance (ANOVA) showed a significant difference in mean VAS scores across beverages (F = 2.93, df = 2.9/86.3, P = 0.039, Greenhouse-Geisser corrected); the effect size for the difference was small (ηp2 = 0.092). A post hoc Bonferroni shows significantly higher VAS scores with whiskey compared with vodka (P = 0.029), whereas the scores were similar across other beverages. A two-way repeated-measures ANOVA for interaction between type of alcoholic beverages and activity was not significant (F = 2.67, df = 2.6/76.6, P = 0.061, Greenhouse-Geisser corrected). We created a standardized alcohol-related image database for studying cue-reactivity paradigms in individuals with alcohol use disorder (AUD). Further research is needed to validate the impact of image features on cue reactivity.

Construction of XAFS standard sample database at BL14B2 in SPring-8

We have built the XAFS Standard Sample Database, which collects XAFS spectral data of standard samples measured at BL14B2 in SPring-8 and classifies them systematically (element, absorption edge, and lattice plane). The database contains XAFS spectral data of 36 elements, 720 samples, and 1604 experimental spectra (as of 10.1.2022). All of the standard samples registered in the database were commercial reagents for which product information such as purity, composition, and lot number can be obtained. Samples unstable in air, such as deliquescent and hygroscopic ones, were prepared in a glove box and sealed in a gas barrier bag or aluminum laminated bag. XAFS measurements for the standard samples were conducted in the transmission mode at BL14B2 in SPring-8. The XAFS spectra were checked by comparing them with those of other samples, those reported in past papers, and those measured at other Japanese XAFS facilities. The database was developed by the data transfer system BENTEN. The database can be used by anyone with an authentication on a personal email address. The database can be used not only for reference data to analyze XAFS spectra of unknown samples but also for the preliminary examination of XAFS measurements and comparison with data in other facilities.

Intelligent material classification of traditional Chinese medicine based on semantic analysis

A method for material classification of traditional Chinese medicines based on the physical properties of powder has been established by our research group. This method involves pre-treatment of traditional Chinese medicine decoction pieces, powder preparation, and determination of physical properties, being cumbersome. In this study, the word segmentation logic of semantic analysis was adopted to establish the thesaurus and local standardized semantic word segmentation database with the macroscopic and microscopic characteristics of 36 model traditional Chinese medicines as the basic data. The physical properties of these medicines have been determined and the classification of these medicines is clear in the cluster analysis. A total of 55 keywords for powdery, fibrous, sugary, oily, and brittle materials were screened by association rules and the set inclusion and exclusion criteria, and the weights of the keywords were calculated. Furthermore, the algorithms of the keyword matching scores and the computation rules of the single or multiple material classification were established for building the intelligent model of semantic analysis for the material classification. The semantic classification results of the other 35 TCMs except Pseudostellariae Radix(multi-material medicine) agreed with the clustering results based on the physical properties of the powder, with an agreement rate of 97.22%. In model validation, the prediction results of semantic classification of traditional Chinese medicines were consistent with the clustering results based on the physical properties of powder, with an agreement rate of 83.33%. The results showed that the method of material classification based on semantic analysis was feasible, which laid a foundation for the development of intelligent decision-making technology for personalized traditional Chinese medicine preparations.

Review and prospects of international clinical research in critical care medicine in 2023

The main clinical research advances of critical care in 2023 includes: new trials of Chinese herbal medicine, hydroxocobalamin (vitamin B12), methylene blue as well glucocorticoids have shown the potential to improve outcomes of patients with sepsis and septic shock; international committees launched new global definition and managing recommendations for acute respiratory distress syndrome (ARDS). Besides, a cluster of new evidences has emerged in many aspects as following: fluid control strategy in sepsis (restrictive/liberative), antibiotic infusion strategy (continuous/intermittent), oxygen-saturation targets for mechanical ventilation (conservative/liberative), blood pressure targets after resuscitation from out-of-hospital cardiac arrest (hypotension/hypertension), blood pressure targets after successful stroke thrombectomy (intensive/conventional), and nutritional support strategies (low protein-calories/conventional protein-calories, fasting/persistent feeding before extubation). Thus, given above progress, carrying out high -quality domestic multi-center clinical registration researches, constructing shareable standardized databases, as well raising public awareness of sepsis, should be the essential steps to improve our level of intensive care medicine.

Heat Transfer-Inspired Network for Image Super-Resolution Reconstruction.

Image super-resolution (SR) is a critical image preprocessing task for many applications. How to recover features as accurately as possible is the focus of SR algorithms. Most existing SR methods tend to guide the image reconstruction process with gradient maps, frequency perception modules, etc. and improve the quality of recovered images from the perspective of enhancing edges, but rarely optimize the neural network structure from the system level. In this article, we conduct an in- depth exploration for the inner nature of the SR network structure. In light of the consistency between thermal particles in the thermal field and pixels in the image domain, we propose a novel heat-transfer-inspired network (HTI-Net) for image SR reconstruction based on the theoretical basis of heat transfer. With the finite difference theory, we use a second-order mixed-difference equation to redesign the residual network (ResNet), which can fully integrate multiple information to achieve better feature reuse. In addition, according to the thermal conduction differential equation (TCDE) in the thermal field, the pixel value flow equation (PVFE) in the image domain is derived to mine deep potential feature information. The experimental results on multiple standard databases demonstrate that the proposed HTI-Net has superior edge detail reconstruction effect and parameter performance compared with the existing SR methods. The experimental results on the microscope chip image (MCI) database consisting of realistic low-resolution (LR) and high-resolution (HR) images show that the proposed HTI-Net for image SR reconstruction can improve the effectiveness of the hardware Trojan detection system.

Uncertainty analysis and updated user guidance for interspecies correlation estimation models and low toxicity compounds.

Interspecies correlation estimation (ICE) models are log-linear relationships of acute sensitivity between two species that estimate the sensitivity of an untested species from the known sensitivity of a surrogate. As ICE model use increases globally, additional user guidance is required to ensure consistent use across chemicals and applications. The present study expands ICE uncertainty analyses and user guidance with a focus on low toxicity compounds whose acute values (i.e., reported as mg/L) can be greater than those used to develop a model. In these cases, surrogate values may be outside the ICE model domain and require additional extrapolations to predict acute toxicity. We use the extensive, standardized acute toxicity database underlying ICE models to broadly summarize inter-test variability of acute toxicity data as a measure by which model prediction accuracy can be evaluated. Using the data and models found on the USEPA Web-ICE (www3.epa.gov/webice), we created a set of "truncated" models from data corresponding to the lower 75th percentile of surrogate toxicity. We predicted toxicity for chemicals in the upper 25th percentile as both μg/L beyond the model domain and converted to mg/L (i.e., "scaled" value) and compared these predictions with those from cross-validation of whole ICE models and to the measured value. For ICE models with slopes in the range 0.66-1.33, prediction accuracy of scaled values did not differ from the accuracy of the models when data were entered as μg/L within or beyond the model domain. An uncertainty analysis of ICE confidence intervals was conducted and an interval range of two orders of magnitude was determined to minimize type I and II errors when accepting or rejecting ICE predictions. We updated the ICE user guidance based on these analyses to advance the state of the science for ICE model application and interpretation. Integr Environ Assess Manag 2024;20:1554-1565. Published 2023. This article is a U.S. Government work and is in the public domain in the USA.

Synthesis of nano-crystallite hydroxyapatites in different media and a comparative study for estimation of crystallite size using Scherrer method, Halder-Wagner method size-strain plot, and Williamson-Hall model

Hydroxyapatite (HAp) [Ca10(PO4)6(OH)2] is remarkably similar to the hard tissue of the human body and the uses of this material in various fields in addition to the medical sector are increasing day by day. In this research, mustered oil, soybean oil, as well as coconut oil were employed as liquid media for synthesizing nanocrystalline HAp using a wet chemical precipitation approach. The X-ray diffraction (XRD) study verified the crystalline phase of the HAp in all the indicated media and discovered similarities with the standard database. Several prominent models such as the Scherrer's Method (SM), Halder-Wagner Method (HWM), linear straight-line method (LSLM), Williamson-Hall Method (W-M), Monshi Scherrer Method (MSM), Size-Strain Plot Method (SSPM), Sahadat-Scherrer Method (S–S) were applied for the determination of crystallite size. The stress, strain, and energy density were also computed from the above models. All the models, without the Linear straight-line technique of Scherrer's equation, resulted in an appropriate value of crystallite size for synthesized products. The calculated crystallite sizes were 6.5 nm for HAp in master oil using Halder-Wagner Method, and 143 nm for HAp in coconut oil using the Scherrer equation which were the lowest and the largest, respectively.

Prediction of stability constants of metal-ligand complexes by machine learning for the design of ligands with optimal metal ion selectivity.

The new LOGKPREDICT program integrates HostDesigner molecular design software with the machine learning (ML) program Chemprop. By supplying HostDesigner with predicted logK values, LOGKPREDICT enhances the computer-aided molecular design process by ranking ligands directly by metal-ligand binding strength. Harnessing reliable experimental data from a historic National Institute of Standards and Technology (NIST) database and data from the International Union of Pure and Applied Chemistry (IUPAC), we train message passing neural net algorithms. The multi-metal NIST-based ML model has a root mean square error (RMSE) of 0.629 ± 0.044 (R2 of 0.960 ± 0.006), while two versions of lanthanide-only IUPAC-based ML models have, respectively, RMSE of 0.764 ± 0.073 (R2 of 0.976 ± 0.005) and 0.757 ± 0.071 (R2 of 0.959 ± 0.007). For relative logK predictions on an out-of-sample set of six ligands, demonstrating metal ion selectivity, the RMSE value reaches a commendably low 0.25. We showcase the use of LOGKPREDICT in identifying ligands with high selectivity for lanthanides in aqueous solutions, a finding supported by recent experimental evidence. We also predict new ligands yet to be verified experimentally. Therefore, our ML models implemented through LOGKPREDICT and interfaced with the ligand design software HostDesigner pave the way for designing new ligands with predetermined selectivity for competing metal ions in an aqueous solution.

An International Standard RWD Database Designed - Taiwan Experience.

The use of Real-World Data (RWD) in medical data analysis is nowadays required. RWD may come from electronic health records (EHRs), insurance claims, medical products, Internet of Things sensors, health screenings, etc. The goal of RWD is that the data used for analysis should not be affected by environmental variables, experimental control, research context settings, etc. RWD can effectively reduce the cost and improve the accuracy of medical research. The clinical data and patient self-report are integrated, cleaned and pre-processed, and the data format is unified and standardized by international standard formats to provide a structured database for clinical research by this study. An international standard real-world research database was established through the breast cancer database of a medical center in Taipei.

Automatic Classification of Pavement Type and Service Age Benchmarked with Standard Texture Databases Using the Machine Learning Method: A Pilot Study

Pavement intelligent management systems have attracted considerable interest from researchers. However, various service conditions of pavement surface concerning the pavement type, texture service age, and so forth, inhibit a universal algorithm that is feasible for all cases. In this regard, the automatic classification of pavement type and service age is an essential premise to unblock the bottleneck stated above. Based on the surface texture data, a pilot study of the automatic classification approach to identify pavement surface textures using convolutional neural networks (CNNs) is presented. For comparison, the efficiency of the support vector machine (SVM) is also investigated. In total, three cases, (i) pavement types, (ii) texture service ages, and (iii) a combination of (i) and (ii), are involved in the automatic classification. The results indicate that the CNN outperforms the SVM, and the CNN models show a favorable classification accuracy for the above three cases with 93.0%, 81.1%, and 83.8%, respectively. In conclusion, the CNN demonstrates a high capability in expressing the pavement texture features and achieves satisfactory identification results for pavement surface types, but is inferior for texture service age. It is promising that the presented results could serve as a foundational exploration in the automatic identification of texture service conditions benchmarked with standard texture databases to facilitate pavement management systems.

Rapid Determination of Positive-Negative Bacterial Infection Based on Micro-Hyperspectral Technology.

To meet the demand for rapid bacterial detection in clinical practice, this study proposed a joint determination model based on spectral database matching combined with a deep learning model for the determination of positive-negative bacterial infection in directly smeared urine samples. Based on a dataset of 8124 urine samples, a standard hyperspectral database of common bacteria and impurities was established. This database, combined with an automated single-target extraction, was used to perform spectral matching for single bacterial targets in directly smeared data. To address the multi-scale features and the need for the rapid analysis of directly smeared data, a multi-scale buffered convolutional neural network, MBNet, was introduced, which included three convolutional combination units and four buffer units to extract the spectral features of directly smeared data from different dimensions. The focus was on studying the differences in spectral features between positive and negative bacterial infection, as well as the temporal correlation between positive-negative determination and short-term cultivation. The experimental results demonstrate that the joint determination model achieved an accuracy of 97.29%, a Positive Predictive Value (PPV) of 97.17%, and a Negative Predictive Value (NPV) of 97.60% in the directly smeared urine dataset. This result outperformed the single MBNet model, indicating the effectiveness of the multi-scale buffered architecture for global and large-scale features of directly smeared data, as well as the high sensitivity of spectral database matching for single bacterial targets. The rapid determination solution of the whole process, which combines directly smeared sample preparation, joint determination model, and software analysis integration, can provide a preliminary report of bacterial infection within 10 min, and it is expected to become a powerful supplement to the existing technologies of rapid bacterial detection.

DDoS attack detection in smart grid network using reconstructive machine learning models.

Network attacks pose a significant challenge for smart grid networks, mainly due to the existence of several multi-directional communication devices coupling consumers to the grid. One of the network attacks that can affect the smart grid is the distributed denial of service (DDoS), where numerous compromised communication devices/nodes of the grid flood the smart grid network with false data and requests, leading to disruptions in smart meters, data servers, and the state estimator, ultimately effecting the services for end-users. Machine learning-based strategies show distinctive benefits in resolving the challenge of securing the network from DDoS attacks. Regardless, a notable hindrance in deploying machine learning-based techniques is the requirement of model retraining whenever new attack classes arise. Practically, disrupting the normal operations of smart grid is really discouraged. To handle this challenge effectively and detect DDoS attacks without major disruptions, we propose the deployment of reconstructive deep learning techniques. A primary benefit of our proposed technique is the minimum disruption during the introduction of a new attack class, even after complete deployment. We trained several deep and shallow reconstructive models to get representations for each attack type separately, and we performed attack detection by class-specific reconstruction error-based classification. Our technique experienced rigid evaluation via multiple experiments using two well-acknowledged standard databases exclusively for DDoS attacks, including their subsets. Later, we performed a comparative estimation of our outcomes against six methods prevalent within the same domain. Our outcomes reveal that our technique attained higher accuracy, and notably eliminates the requirement of a complete model retraining in the event of the introduction of new attack classes. This method will not only boost the security of smart grid networks but also ensure the stability and reliability of normal operations, protecting the critical infrastructure from ever-evolving network attacks. As smart grid is advancing rapidly, our approach proposes a robust and adaptive way to overcome the continuous challenges posed by network attacks.