This review describes the main structures and characteristics of several building blocks used to obtain stable, self-assembling nanostructures tailored for numerous biological applications. Owing to their versatility, biocompatibility, and controllability, these nanostructures find application in diverse fields ranging from drug/gene delivery, theranostics, tissue engineering, and nanoelectronics. Herein, we described the different approaches used to design and functionalize these nanomaterials to obtain selective drug delivery in a specific disease. In particular, the review highlights the efficiency of these supramolecular structures in applications related to infectious diseases and cancer.Numerous supramolecular platforms inspired by natural self-assembly are exploited as drug delivery systems. The spontaneous arrangement of single building blocks into inorganic and organic structures is determined and controlled by noncovalent forces such as electrostatic interactions,  Motivated by the extensive presence of self-assembly in nature, researchers have explored the self-assembly process of natural macromolecules to push chemical self-assembly and develop a series of advanced designs with defined structure, size, function, and application , 2. Furtπ-π interactions, hydrogen bonds, van der Waals interactions, and hydrophobic interactions) that are individually very weak (2–250 kJ/mol) compared to covalent bonds (100–400 kJ/mol) [Self-assembly is the natural growth of ordered structures with no assistance from an outer agent , determi kJ/mol) , but tog kJ/mol) . Self-as kJ/mol) . To inve kJ/mol) . In addi kJ/mol) , and to  kJ/mol) .This review aims to focus on the powerful role of finely tuning self-assembly to succeed in obtaining the preferred structures with the desired properties. As bridging the design and prediction of the self-assembly process will certainly have a great influence in medicine; here, a description of the designed self-assembly process is delivered with a report on the structural features and possible applications in infectious diseases and cancer .The review is mainly divided into five parts: (i). self-assembling strategies; (ii). self-assembled nanosystems and corresponding assembly morphology ; (iii). the recent advances in applications for infectious diseases; (iv). the recent advances in applications for cancer; and (v). biophysical techniques to characterize self-assembled nanosystems.Self-assembly is a key theme in contemporary science with great attention given to the identification of the driving forces that regulate this process and to the supramolecular nanostructures obtained and to the physical, chemical, and biological properties that can be attained . The colSeveral morphologies can be obtained according to the building blocks used, for instance, vesicles, micelles, ribbons, helices, spherical nanoparticles, nanorods, nanotubes, and nanofibers, whose complexity increases as a function of their size see .Micelles are supramolecular aggregates of polymeric amphipathic molecules approximately spherical in shape , 20, whiVesicles are self-contained structures consisting of an aqueous compartment surrounded and enclosed by a hydrophobic layer. The tendency of hydrophobic molecules to clump together in an aqueous environment can lead to the spontaneous formation of vesicles . Thus, vSpherical nanoparticles comprise a filled-core material . These kNanotubes are structures forming tubes with an interior hollow space; usually, these cylindrical structures have diameters of ∼1–100 nm . TypicalBuilding blocks for engineering a multiplicity of architectures with distinct morphologies can be achieved from a wide assortment of molecules through noncovalent interactions in each solvent. The main synthetic and biological building blocks are lipids, peptides, dendrimers, cyclodextrins, viruses, nucleic acids, polysaccharides, etc., and according to the building block, different supramolecular assemblies may be obtained, as reported in such as monolayers, bilayers, micelles, and vesicles). The amphiphilic nature of liposomes can be exploited to encapsulate both hydrophilic and hydrophobic drugs [Lipids are composed of a hydrophilic head group and a hydrophobic tail region and represent key building blocks . In factic drugs . A partiic drugs –32. LNP ic drugs .in vivo, as they cause accumulation because of long-term therapies. Dendrimers represent a challenging strategy for the simultaneous delivery of multiple therapeutics to target sites and their inner compartment can be used to accommodate a cargo.Dendrimers are synthetic, well-defined, and monodispersed molecules which arrange with a symmetrically multibranched core-shell architecture and may also assemble, through noncovalent interactions, into more complex structures (nanoparticles and nanofibers) , 35. Denπ–π stacking, etc. and typically confer poor mechanical strength, while covalent cross-linking including free radical polymerization, enzyme-induced cross-linking, etc., confers high mechanical properties [such as chitosan, gelatine, and polynucleotides) or synthetic polymers , poly (ethylene glycol), and poly (sulphobetaine)) [Hydrogels are three-dimensional polymeric porous structures formed by physical or chemical crosslinking and able to house various materials, including small molecules, polymers, and particles and with high water absorption capacities (>90%). Physical cross-linking strategies include hydrophobic interactions, hydrogen bonding, polymerization entanglement, operties . Hydrogeetaine)) . Their petaine)) . As prometaine)) . A key pAmino acids constitute peptides and proteins, and their side chain properties  are responsible of important noncovalent interactions which makes them simple building blocks to exploit for fabricating complex supramolecular assemblies . FurtherViruses-, protein-, and nucleic acid-based supramolecules are interesting building blocks thanks to their shape and size uniformity and their natural functionality through self-assembly . NormallPolymersomes are self-assembled polymers made of synthetic block amphiphiles , which aπ-π stacking interactions, forming a variety of nanostructures featuring different compositions, geometries, and functionalities [Individual nucleic acids such DNAs and RNAs can assemble in a controlled fashion by hydrogen bonds and nalities . RNA ternalities . Anothernalities . Nucleicnalities .The self-assembly of PNA has recently been attracting attention for its many potential applications. PNA-based nanostructures originate from the combination and assembly of nucleic acids and peptide with specific properties . Swenson2, that are used to engineer polysaccharides-based nanoparticles (NPs) [β-1,4-linked N-acetyl-2 amino-2-deoxy-D-glucose and 2-amino-2-deoxy-D-glucose units [α-linkage; the most abundant natural CDs consist of six (αCD), seven (βCD), and eight units (γCD). CDs assume the structure of a truncated cone having a hydrophilic exterior and a hydrophobic interior, which favours the formation of inclusion complex where a lipophilic molecule is entrapped in the hydrophobic CD cavity, while a hydrophilic molecule can interact on its surface. Thus, in the pharmaceutical field, CDs are generally used to enhance solubility and bioavailability of hydrophobic drugs. For instance, in water solution, γCD, thanks to the hydrogen bonding driving force of the external OH groups, can self-assemble to form aggregates in a concentration-dependent manner [γCD aggregates are consistently formed and dissociated in aqueous solutions. The self-assembled γCD may aid in increasing the solubility and enhancing drug permeability through biological membranes.Natural polysaccharides present several advantages, including good biocompatibility and low immunogenicity and toxicity, making them appropriate for engineering materials with biomedical applications. Polysaccharides have significant chemical and structural diversity  and present several functional groups, such as OH, –COOH, and –NHes (NPs) . Amphiphes (NPs) . These pse units , is the se units . For insse units . Being bse units . HA-basese units . Examplet manner . This isGraphene oxides have attracted great attention as carriers for drug delivery because of their exceptional biocompatibility. Furthermore, graphene oxides present tremendous flexibility which allows them to assemble into different shapes such as spherical microcapsules which have a coreshell structure that can effectively load diverse drugs . MachadoA new attractive frontier in the supramolecular chemistry is represented by the self-assembly of preassembled nanoassemblies. This challenging approach allows the construction of multilevel architectures that are highly complicated, featuring high versatility and a wide range of applications. Several kinds of copolymer-preassembled nanoassemblies might be exploited as subunits for higher level self-assembly. Apart from micelles or vesicles, to develop self-assembly from preassembled nanoassemblies, it is necessary to introduce interactions among subunits, which include electrostatic interactions, crystallization-driven contacts, and other kinds of intermolecular forces.β-sheets and coiled-coils motifs derived from some natural proteins such as insulin and α-synuclein showed the capability to self-assemble forming origami. The choice of the kind of protein and correlated peptide motifs can be performed by using specific software.DNA origami nanostructures and supramolecular DNA-based structures possessing self-complementarity present many applications in material science and nanomedicine. The DNA origami technology constitutes a versatile approach in assembling two- and three-dimensional structures of nucleic acid constituents . The useAnyway, although the fabrication of origami by exploiting peptide self-assembly remains still a hard challenge, different studies were performed. For instance, Fletcher and co-workers used two coiled-coil motifs that self-assemble into hollow balls with a diameter of 100 nm forming a single-layered protein grid . SimilarHence, as described above, thanks to their feasibility and superiority, peptide motifs have a significant potential to engineer hybrid origami structures with a wide range of biomedical applications.Drug resistance represents a great uncertainty in current medicine, and new strategies to address infectious diseases are needed without delay to avoid widespread susceptibility against infections that so far have been straightforwardly treated with existing traditional antibiotics/drugs , 77. OneDhumal et al. have demonstrated that an amphiphilic dendrimer made of a long hydrophobic alkyl chain and a small hydrophilic PAMAM dendron carrying distinct terminal functionalities interacts and binds with the bacterial membrane by forming electrostatic interactions; through self-assembly into supramolecular structures, they destroy the bacterial membrane .50 was 100 nM) and HSV-2 (IC50 was 300 nM) at a very early stage of the entry process of infection, preventing the virus from interacting with cellular membranes, and no evidence of cell toxicity at these concentrations was reported [We also reported the efficient use of bifunctional peptidodendrimer orthogonally conjugated with two different antiviral peptides derived from the glycoproteins gH and gB of herpes simplex virus type 1 (HSV-1). The resulting bioconjugate demonstrated no toxicity to cells at biologically relevant concentrations and good inhibition of both the early and the late stages of the herpes infection process . Taralloreported .Moreover, the internal cavity of dendrimers is favourable to encapsulate a huge variety of hydrophobic antiviral drugs, i.e., ritonavir and efavirenz. Pyreddy and co-workers synthesized a fifth-generation efavirenz (EFV) conjugating PEGylated PAMAM dendrimers through an ethylenediamine central core by using Michael addition , 82.Staphilococcus aureus. In particular, the drug was released only when in close proximity to the bacteria [S. aureus with nanomolar affinity [S. aureus biofilms. When the dendron is conjugated to gH625, its efficiency is increased, and the effect on biofilm viability was observed at the lower amount of dendron than the dendron alone [Antibiotics have also been coupled to dendrimers to enhance their antimicrobial activity, half-life, and solubility , 84. Penbacteria . Similaraffinity . In addion alone .Supramolecular assembly of peptides has been widely exploited as an effective method to obtain AMPs with increased stability, antimicrobial efficacy, decreased haemolytic response, and enhanced stability to serum proteins. When dealing with self-assembling of AMPs, it is important to keep in mind that the self-assembling properties are generally associated with their secondary structure and amphiphilicity; consequently, de novo design may guarantee the self-assembling but not necessarily the antimicrobial activity.AMPs rarely self-assemble into supramolecular structures in water solution for their cationicity, while they self-assemble into fibrillar nanostructures or helical bundles in the presence of the membrane environment, favouring an enhancement of their antimicrobial activity . Instead2 heparin-binding Cardin-motif, which exhibited enhanced activity against Gram-negative bacteria above the critical micellar concentration (CMC) [12-VVAGKKKGRW-NH2 and KKKGRW-NH2 nanofibers with their corresponding peptide without C12 [The antimicrobial activity can increase thanks to the conjugation of fatty acids of varying length that promote the AMP aggregation , 93. Chaon (CMC) . Similarhout C12 . In conthout C12 .S. aureus, P. aeruginosa, and twenty resistant K. pneumoniae clinical isolates, as well as against some clinical Candida albicans species [Instead, self-assembling antimicrobial peptides derived from Temporin L designed by Bellavita et al. showed improved bacterial membrane interaction with consequently better antimicrobial activity and reduced cytotoxicity. In particular, the authors showed that the lipid tail of 5-carbon atoms determined a target cell specificity and a strong oligomerization in a bacterial membrane-mimicking environment and not in an aqueous solution . This li species , 99.P. aeruginosa and the fungus C. albicans. In addition, these self-assembled nanostructures also increase the stability and half-life of WMR.A novel, versatile platform for the delivery of AMPs was developed in our laboratory . As prooThe development of a supramolecular platform that acts by making holes in the cell membrane and thereby destroying the bacteria represents a very promising and alternative strategy, since bacteria are not able to develop resistance because they should completely change their membrane.Self-assembled nanomaterials endowed with nanorange sizes of 1–100 nm are widely used in cancer therapeutics . The druAmong the different nanomaterials listed above, dendrimers (such as PAMAM dendrimers) are carriers that efficiently deliver and control the anticancer drug release at the tumor site. Recently, Lewinska and co-workers functionalized the PAMAM dendrimer with lapatinib and fulvestrant drugs which act as inhibitor of EGFR and HER2 tyrosine kinases and estrogen receptor degrader, respectively . The useLike dendrimers, liposomes are also a popular platform used for the targeting and controlling the release of anticancer drugs thanks to their ability to accumulate in tumor sites via the enhanced permeability and retention (EPR) effect . In 1995In addition, due to their versatility and possibility of being functionalized, liposomes showed the ability to bypass tumor-driven resistance. For example, liposomes with encapsulated Doxo and decorated with gH625 showed a great ability to internalize in Doxo-resistant cells, inhibiting the growth of lung adenocarcinoma cancer cells . Similar19 lipid tail and hexa-alanine sequence) and hydrophilic (-GDDS- and -GKRS-) domains [Among the different types of materials, self-assembled peptide-based platforms represent one of the most recent innovations in cancer therapy , 112. Th domains . Both pe domains .Moreover, self-assembled peptide-based hydrogels also represent an important modulable and biocompatible nanomaterial used in cancer therapy. The best example is the peptide RADA16 made of repeated units of -Arg-Ala-Asp-Ala-sequence, which is able to form nanofibers with a width of 3–8 nm through electrostatic interactions , while aSelf-assembled peptide nanoparticles were recently developed against hepatocellular carcinoma cells to be used for photodynamic therapy. Co-assembled nanoparticles were obtained through the combination of an aromatic short peptide with a porphyrin derivative for enhanced antitumor treatment .Among the different supramolecular platforms mentioned above, polymersomes are considered a great promise due to their ability to overcome the main limitations of liposomes in drug delivery. These nanosystems own a longer half-live and a better capacity to accumulate in tumor site, releasing the drug. Significant results with polymersomes in the anticancer combined therapy were obtained by Colley et al.  The authIn this section, we briefly reported how supramolecular nanoplatforms can be designed and customized to make them able to respond at different tumor environments and obtain a targeted drug delivery. However, these nanoplatforms can also be tailored with MR imaging contrast agents for the use in cancer diagnosis.The complex process of self-assembly makes the selection of the methodology to use for biophysical characterization a key factor; actually, several experimental techniques were established to study self-assembled nanosystems with the main differences being related to the nature and size of the nanomaterial, the sensitivity, the type, and the resolution of the knowledge provided. Here, we briefly describe the most used methodologies, considering that they should always come together in a biophysical characterization of self-assembled materials. Key techniques include super-resolution fluorescence microscopy ), transmission electron microscopy (TEM), atomic force microscopy (AFM), small-angle neutron scattering (SANS), circular dichroism (CD), Fourier transform infrared spectroscopy (FTIR), and nuclear magnetic resonance (NMR)).The use of TEM, AFM, and optical microscopy provides evidence about morphologies and geometric parameters, such as size and diameter. Spectroscopic methods such as CD, NMR, and FTIR are suitable to determine the secondary structures of self-assembled materials. Moreover, scattering techniques such as SANS are broadly used, but elucidation of results might be difficult.One of the most used methodologies to analyse the establishment of aggregated structures is fluorescence spectroscopy. Fluorophores such as Nile Red and Thioflavin T can be exploited to investigate their diverse environment when in aqueous solutions and when in the hydrophobic core of the self-assembled structure. In addition, labelling of peptides and/or lipids with a fluorescent tag may be used to analyse the kinetics of self-assembly. Tryptophan (Trp) residues are present or are added to peptides for their intrinsic fluorescence, which is very sensitive to the polarity of the environment. Furthermore, supplementary data may be acquired using polar Trp quenchers such as iodide or acrylamide, while brominated or methyl-coumarin-labelled phosphocholines can be used to quench the Trp in hydrophobic environments.Regarding the STORM technique, self-assembling nanosystems are firstly conjugated with fluorescent dyes, which are then activated by lasers to induce a blinking state . The posAFM consents to determining the density of self-assembled structures which usually appear to be higher than that determined by STORM; in fact, while STORM images are acquired in bulk solution, AFM images are recorded after applying the drying process to cause artificial condensation onto the substrate surface. Clearly, AFM does not provide any chemical information of the analysed system.TEM is chosen for the nanometric investigation of the structure and morphology of self-assembled structures . The strSANS is a nondestructive technique based on the intensity of a neutron beam scattered from a solution . First, In case of peptides, the secondary structure as well as the stability of the self-assembled nanosystem to dilution, different pH values, and different ionic strength can easily be obtained from CD. Furthermore, FTIR spectroscopy can be used to study self-assembly upon drying nanostructures, generating great insight into the dynamics of these processes. The molecular information gained by FTIR allows the determination of the structural and conformational changes of functional groups .NMR is generally used to complement CD to determine the secondary structure of self-assembled aggregates. Solution measurements are limited to the mobility of molecules because the assembly reduces the intensity and shifts the frequency, causing difficulties in interpreting the spectra ; while sIn conclusion, despite the huge assortment of experimental techniques that are available, the full characterization of interactions in self-assembled nanosystems is still a complex process which needs the use of several complementary experimental techniques.ad hoc as a function of their use.In summary, we presented in detail supramolecular materials exhibiting wide applications in different fields ranging from drug delivery to biosensing, bioimaging, and tissue engineering. Self-assembly is a phenomenon controlled by internal and external molecular interactions such as hydrogen bonding, hydrophobic, and electrostatic interactions. These interactions with other factors provide the way for forming the kind of nanostructure such as fibers, nanotubes, vesicles, and micelles. The most common nanomaterials, such as liposomes, micelles, and dendrimers, are extensively used as platforms for achieving targeted drug delivery in anticancer and antimicrobial fields see . The pos


Dear Editor,Compared with traditional technologies affecting gene expression, changing DNA sequences of target genes is one of the most outstanding characters of CRISPR . Single-guide RNAs (sgRNAs) guiding endonuclease Cas to target sites is a crucial step of CRISPR-Cas system for changing DNA sequences. An ideal sgRNA should only bind to the target gene. However, similar sequences of non-target sites can also be recognized leading to off-target effects . How to Compared with previous tools , CRISPR-GE (http://skl.scau.edu.cn/targetdesign/), CRISPR-P (http://crispr.hzau.edu.cn/cgi-bin/CRISPR2/CRISPR), E-CRISP (http://www.e-crisp.org/E-CRISP/index.html), and MultiTargeter (http://multicrispr.net/basic_input.html) to design sgRNAs. After that, the five sgRNAs of each gene with the highest scores were collected to construct the testing database. Comparing the sgRNA-design results of CIDP with the testing database, we found that most of sgRNAs designed by CIDP can be found in the testing database (87.4%, In order to evaluate design results of CIDP, we randomly selected 1000 genes of e 87.4%, . Moreovee 87.4%, . These rCIDP utilizes genomic sequences to build the database containing short segments whose sequences are unique across the whole genome. When designing sgRNAs, CIDP will extract candidate sgRNAs from gene sequences based on the selected PAM model, and then filter these sgRNAs by using the built database. Furthermore, CIDP will align the remaining sgRNAs to genome sequences to identify potential off-target sites and calculate off-target rates. Therefore, we believe that the sgRNAs designed by CIDP should be highly reliable, whereas, no matter the building or aligning process, the two operations were all performed at the level of genome. In general, a large amount of computer memory is required for supporting genome-level operations. This is an important reason why the tools performing genome-level operations can only be run on Linux servers , 11. HowIn order to solve the efficiency problem of CIDP as much as we could, we developed a Linux version of CIDP that can be run on Linux servers. Moreover, the built database can be reused. Therefore, it is not required for users to build the database repeatedly. The CRISPR system is a common technology for wet experiments, and a species may be studied for several years by some wet labs. In this situation, the built database can also be reused by researchers in the same lab. It is truly hard to finish genome-level jobs under the condition of limited memory with high efficiency. Increasing CIDP efficiency is an important task for us in the future. In the next version, we will try to design functions in a multi-threaded or multi-processed manner which may elevate the design efficiency, even though these manners still require amount of memory support.Web_Material_uhad092Click here for additional data file.


The lungs are critical components of the respiratory system because they allow for the exchange of oxygen and carbon dioxide within our bodies. However, a variety of conditions can affect the lungs, resulting in serious health consequences. Lung disease treatment aims to control its severity, which is usually irrevocable. The fundamental objective of this endeavor is to build a consistent and automated approach for establishing the intensity of lung illness. This paper describes MixNet-LD, a unique automated approach aimed at identifying and categorizing the severity of lung illnesses using an upgraded pre-trained MixNet model. One of the first steps in developing the MixNet-LD system was to build a pre-processing strategy that uses Grad-Cam to decrease noise, highlight irregularities, and eventually improve the classification performance of lung illnesses. Data augmentation strategies were used to rectify the dataset’s unbalanced distribution of classes and prevent overfitting. Furthermore, dense blocks were used to improve classification outcomes across the four severity categories of lung disorders. In practice, the MixNet-LD model achieves cutting-edge performance while maintaining model size and manageable complexity. The proposed approach was tested using a variety of datasets gathered from credible internet sources as well as a novel private dataset known as Pak-Lungs. A pre-trained model was used on the dataset to obtain important characteristics from lung disease images. The pictures were then categorized into categories such as normal, COVID-19, pneumonia, tuberculosis, and lung cancer using a linear layer of the SVM classifier with a linear activation function. The MixNet-LD system underwent testing in four distinct tests and achieved a remarkable accuracy of 98.5% on the difficult lung disease dataset. The acquired findings and comparisons demonstrate the MixNet-LD system’s improved performance and learning capabilities. These findings show that the proposed approach may effectively increase the accuracy of classification models in medicinal image investigations. This research helps to develop new strategies for effective medical image processing in clinical settings. DL algorithms (DL) have transformed the area of medicinal image investigation, promising advances in lung disease identification and classification. Based on significant research articles in the field, this introduction provides a general idea of the application of DL models in identifying and categorizing various lung illnesses such as pneumonia, tuberculosis (TB), COVID-19, and lung cancer. Wang et al.  presenteAnthimopoulos et al.  developeThis extensive study gives important insights into the creation of DL models for the identification of pneumonia, TB, COVID-19, and lung cancer. They show how DL can reliably detect and categorize lung disorders, resulting in improved patient outcomes and more effective healthcare administration, as shown in Despite the use of advanced technologies for image processing, both during and after acquisition, defining lung features from images associated with normal, COVID-19, pneumonia, tuberculosis, and lung cancer remains difficult due to the struggle in emplacement and extracting lesion features associated with lung diseases.Professional medical annotations are restricted in publicly available datasets encompassing a varied array of normal, COVID-19, pneumonia, tuberculosis, and lung cancer-related damage variables. As a result, computerized systems have difficulty in precisely diagnosing the symptoms of certain disorders.Despite the development of various approaches for diagnosing lung disorders from photographs, such as normal, COVID-19, pneumonia, tuberculosis, and lung cancer, substantial hurdles remain:As a result, the foremost aim of this research is dual. It intends to evolve a complete dataset for classifying normal, COVID-19, pneumonia, tuberculosis, and lung cancer (abbreviated Pak-Lungs). Second, the study aims to create a comprehensive multi-layered DL model capable of autonomously interpreting pictures relevant to lung disorders, especially in the setting of lung-associated ailments. To do this, a multi-layered MixNet system is used to build a MixNet-LD system with dense blocks. By fully training this MixNet-LD system, it becomes skilled at reliably recognizing lung-associated illnesses on diverse pictures connected to lung-associated maladies, including anatomic component recognition. An experienced pulmonologist discovered these illnesses and anatomical traits. This work is significant because it proposes a revolutionary categorization system for lung disorders with potential real-world implications for medical diagnosis and therapy.The researchers created an enormous dataset called “Pak-Lungs” for this study by gathering 6000 photos from Pakistani hospitals and other internet resources. This large dataset was critical in allowing the trained model to attain exceptionally high classification accuracy.The study used dense blocks and trained MixNet CNN to build the MixNet-LD system, resulting in a multi-layered architecture capable of efficiently handling the classification issue.The MixNet-LD model’s system design includes four extra layers to identify lung-related disorders. The CNN model is used to extract lung-related lesion characteristics, which are subsequently enlarged upon using the dense block approach.The method proposed in this work for categorizing lung disorders is based on the deep features and phases of colour space, which constitute the foundation of the approach. According to the author, this is the first effort to develop a computerized technique that outperforms existing techniques in detecting normal, COVID-19, pneumonia, tuberculosis, and lung cancer disorders.Our systems outperformed the proposed approaches in the current literature with a much higher accuracy of 95%.We proposed a new DL model inside this framework to handle the difficulty of identifying diverse lung illnesses. In addition, we give Pak-Lungs a freshly curated dataset acquired from hospitals in Pakistan. The following are the noteworthy contributions of the MixNet-LD system:The paper is designed as follows: Lung illnesses, such as pneumonia, TB, COVID-19, and lung cancer, are major public health issues globally, resulting in considerable morbidity and death rates . The earIn many countries, chest computed tomography (CT) and X-ray scans are reasonable for identifying COVID-19. Identifying COVID-19, on the other hand, is a complicated job that requires clinical imaging of patients ,14. LungSeveral studies have used CT scans to design and test completely automated COVID-19 detection frameworks. COVID-19 neural network techniques were utilized to obtain graphical information from volumetric chest CT images. The findings show that this strategy outperforms previous approaches. In related work, pre-trained model-based CNNs such as Inception-ResNetV2, ResNet152, ResNet50, InceptionV3, and ResNet101 were used to detect COVID-19 pneumonia based on CXR pictures. ResNet50 produced the most accurate categorization results among these models [Furthermore, COVID-19 patients were identified using the Details Relation Extraction Neural Network, a DL-based CT diagnostic technique. The model made valid distinctions with a recall of 0.93, an AUC of 0.99, and an accuracy of 0.96. According to the findings, DL based on CT scans could be helpful in diagnosing COVID-19 patients and automatically recognizing potentially problematic alterations. Another study used a transformed MobileNet and a ResNet architecture to classify COVID-19 CXR pictures. To avoid the gradient vanishing problem, this methodology dynamically merged characteristics from different CNN layers, and the suggested approaches surpassed current techniques with accuracies of 99.3% on the CT image dataset and 99.6% on the CXR images .Several studies  have buiOne study used an image segmentation approach to classify chest CT scans as pneumonia, COVID-19, or typical diseases. Four CNN-based learners, a transformed stack ensemble model, and I Bayes as the meta-learner were used in the technique. On typical datasets based on CT scans, this strategy surpassed current approaches with an accuracy of 0.9867 and a Kappa of 0.98. The proposed method eliminates the requirement for manual labeling while reliably detecting COVID-19 infections and excluding non-COVID-19 cases . PositivKernel principal component analysis was used to decrease the pre-trained EfficientNet models. A feature fusion approach was used to merge the extracted features. Stacked ensemble meta-classifiers were used to divide the model into two phases. Predictions were created in the first stage using a support vector machine (SVM) and a random forest, then combined and input into the second stage. During the second step, a logistic regression classifier classified the X-ray and CT data into COVID and non-COVID. Compared to previous CNN-based pre-trained models, the new model outperformed them, making it a viable tool for doctors in point-of-care diagnostics .In a similar study, researchers classified COVID-19-infected individuals using ResNet32 and a deep transfer learning approach. The experimental results demonstrated that their COVID-19 classifier outperformed earlier supervised learning models, producing improved outcomes . AnotherFor both binary and multiclass classification, a CNN was utilized. The model was trained using 3877 CT and X-ray images, including 1917 from COVID-19 patients. The binary classifier has a 99.64% accuracy, a 99.58% recall, a 99.56% precision, a 99.59% F1-score, and a 100% ROC. The recommended approach achieved 98.28% accuracy, 98.25% recall, 98.22% precision, 98.23% F1-score, and 99.87% ROC using 6077 pictures, comprising 1917 COVID-19 patients, 1960 healthy persons, and 2200 pneumonia patients .Through transfer learning approaches, CNN with pre-trained weights was used to categorize COVID-19, pneumonia, and healthy persons, accurately identifying those with active SARS-CoV-2 and pneumonia, a noteworthy discovery in that work .Using CT images of benign and malignant lung nodules from the LIDC database, CNN, DNN, and sparse auto-encoder deep neural networks were used to identify lung cancer calcification, reaching an accuracy of 84.15%, sensitivity of 83.96%, and specificity of 84.32% . CNN wasUsing RetinaNet and Mask R-CNN as an ensemble, machine learning was used to detect and localize pneumonia in CXR, attaining a recall of 0.793 for a large dataset . TransfeOne researcher created a CNN model from the ground up to extract and categorize information from chest X-ray pictures of people sick with pneumonia to address issues associated with medical image analysis. Due to the scarcity of pneumonia datasets, different data augmentation procedures utilized to enhance the training of the suggested model and validation classification accuracy, resulting in a substantial precision of 0.94814 during the validation phase . In a diFurthermore, researchers constructed a DCNN model to identify TB using a CXR dataset from the National Library of Medicine, Shenzhen No. 3 Hospital. The datasets that were analyzed were from a separate population’s non-TB-specific chest X-ray dataset. For the two datasets, the DCNN provided AUC values of 0.9845 and 0.8502, respectively, whereas the supervised DCNN model in the CXR dataset had a lower AUC of 0.7054. In the CXR dataset, the final DCNN model successfully identified 36.51% of the abnormal radiographs associated with TB . AdditioOther research presented an ensemble approach based on three well-known designs: AlexNet, GoogleNet, and ResNet. For training and testing, a new classifier for tuberculosis classification was created from scrape utilizing a pooled dataset of widely available ordinary datasets. The suggested strategy outperformed most current algorithms with an accuracy of 88% and an AUC of 0.93 . The extThe COVID-19 pandemic has resulted in an increase in cases and mutations over the last three years, making it difficult to distinguish COVID-19 from other lung illnesses. To avoid ambiguity, this research applies a unique neutrosophic technique, categorizing illnesses into true (T), false (F), and indeterminate (I) categories. The study achieves an impressive 97.33% accuracy in distinguishing infections using image pre-processing and deep learning models such as ResNet-50, VGG-16, and XGBoost, demonstrating the potential of the neutrosophic approach for precise medical imaging diagnosis in the context of the pandemic .This research describes a deep learning system for detecting pulmonary nodules in CT images. The method combines VGG-SegNet for nodule mining with pre-trained deep learning-based classification, which employs both deep and handmade features such as GLCM, LBP, and PHOG. Experiments on the LIDC-IDRI and Lung-PET-CT-Dx datasets reveal that when paired with these characteristics, the VGG19 architecture achieves an amazing 97.83% accuracy with the SVM-RBF classifier, providing a potential approach for accurate and automated nodule detection .The suggested method shows good results in improving replicated picture quality, removing overlapping patterns, and emphasizing distinct ridge features. With this creative use of deep learning methods to picture reconstruction, authentication systems that depend on distinctive individual features might be greatly enhanced .This work investigates the use of deep learning models, namely VGG-16 and DenseNet-169, for the classification of lung disorders using X-ray pictures, with a particular emphasis on pneumonia, tuberculosis, and COVID-19, as well as a “normal” category. The results show that these models have the capacity to reliably diagnose these diseases, with DenseNet-169 surpassing VGG-16 with a noteworthy accuracy of 91%. Given the widespread prevalence of lung illnesses, particularly in resource-limited locations, such deep learning algorithms show potential for improving early detection and patient outcomes, adding to the larger global effort to battle lung diseases. Further research and clinical validation may pave the path for these models to be used in real-world healthcare settings .By restricting the study region, this research investigates the relevance of medical picture segmentation for effective illness detection. It presents a unique technique to picture segmentation based on the heuristic Red Fox Optimization Algorithm (RFOA). This approach automates the selection of threshold parameters by transforming chosen pixels to black or white depending on a threshold value and counting the number of occurrences. The study modifies RFOA for image analysis, tests it on lung X-ray pictures, and examines its benefits and drawbacks .The problem of distinguishing COVID-19 from other chest illnesses is addressed in this research, which proposes a framework for diagnosing 15 chest disorders, including COVID-19, using chest X-ray pictures. For feature extraction, the methodology utilizes deep learning-based CNNs and transfer learning, considerably boosting accuracy for COVID-19 diagnosis while preserving strong prediction for other chest disorders. When compared to current models, the results indicate promise .Within the scope of this paper, a new framework called MixNet-LD is developed, which mixes CNN (MixNet) with dense blocks. The MixNet-LD method is used to categorize lung illness pictures into normal, pneumonia, tuberculosis (TB), COVID-19, and lung cancer. The dense blocks approach is used inside the MixNet-LD system to extract valuable attributes. Transfer learning of dense blocks is used to train on lung-related defects. The MixNet-LD system includes critical mechanisms for detecting lung disease pictures and recognizing the problems described above. The systematic phases are visually depicted in The advancement of an enhanced pre-trained MixNet model incorporating dense blocks represents a notable progression within the domains of deep learning and computer vision. This innovative approach leverages the advantages of two known architectural paradigms, namely MixNet and ResNet, in order to develop a unique hybrid model that holds promise for surpassing its predecessors. MixNet’s ability to efficiently extract features and ResNet’s strong training stability and transferability can be used together to improve performance across a wide range of tasks and datasets. This paradigm is characterized by its ability to adapt to specific domains or activities, facilitated by the deliberate incorporation of intricate components.The practice of adapting tasks to specific needs is particularly valuable in situations where generic models do not achieve optimal performance. One notable characteristic of the innovative model is its extensive empirical validation. In numerous comprehensive studies conducted on diverse datasets and real-world applications, it has consistently demonstrated superior performance compared to traditional methods. The significance of this robust empirical evidence is underscored within the realms of deep learning and computer vision. Furthermore, the model has high resource efficiency, making it particularly ideal for deployment in resource-constrained settings such as edge devices, where computational and memory constraints are crucial. Aside from better efficiency, the model is remarkable for improved interpretability, which is important in industries like healthcare and autonomous driving. It clarifies how it makes decisions by incorporating thick blocks, which may be critical for trust- and safety-sensitive applications.The inclusion of the “upgraded pre-trained MixNet model with dense blocks” is a novel and significant contribution to the field of deep learning research. Many computer vision tasks choose this method because it combines the best parts of MixNet and ResNet in a way that works well. It also has good interpretability, uses few resources, and has been proven to work in real life. Furthermore, this approach demonstrates potential for driving advancements in both efficiency and performance within the field of computer vision.Moreover, the term “original MixNet model” pertains to the foundational structure of the research article or model at its initial publication. In computer vision applications, the architecture commonly comprises a combination of mixed-depthwise convolutions, squeeze-and-excitation blocks, and other relevant components. These elements are designed to effectively and precisely extract features. This model undergoes training either from its initial state or by fine-tuning, with the objective of fulfilling a particular goal, and this training is conducted using a specified dataset.The inclusion of dense blocks in the network design of the upgraded pre-trained MixNet model implies that the original MixNet architecture has undergone modifications or expansions. One potential modification could involve the incorporation of dense connections, sometimes known as skip links, across layers. The ResNet architecture made this design idea well-known, and it has proven to be highly effective in the field of deep neural networks. The network has been enhanced by including dense blocks, thereby integrating elements from both the MixNet and ResNet architectures. The fusion of architectures in the model potentially allows for the integration of advantages from both MixNet and ResNet. This includes the capacity to rapidly extract features from MixNet and enhance training stability through the utilization of ResNet.This suggests that the initial design of MixNet was modified to include dense connections, differentiating it from the standard MixNet architecture. The notable aspect in this context pertains to the integration of compact blocks inside the MixNet framework, which, as mentioned earlier, has the potential to enhance the stability of training, facilitate transfer learning, and improve interpretability. It is imperative to acknowledge that the unique implementation and design choices employed in the fusion of MixNet and ResNet components may vary; thus, the precise characteristics of the “enhanced pre-trained MixNet model with dense blocks” would depend on the particulars of this integration. Moreover, it is imperative to empirically evaluate the effectiveness of this enhanced model through rigorous experimentation on pertinent datasets and tasks.A dataset named Pak-Lungs, which contains 13,313 photos, was used to train and estimate the MixNet-LD model. Images were obtained privately from reputed eye facilities in Pakistan through personal sources. Patients’ and physicians’ permission and willingness to provide data were obtained. It was mutually agreed by the stakeholders that data would be supplied anonymously and that no clinical data would be shared; hence, these conditions kept patient data hidden while making it accessible for study. Both Pak-Lungs and well-known online sources  were useMeticulously inspected 13,313 lung photographs are shown in Using data from three separate sources, the photos were downsized to a more usual dimension of Chest CT-Scan is a composite dataset made up of photos of chest cancers such as adenocarcinoma, large cell carcinoma, squamous cell carcinoma, and normal cells. The data were organized into training, testing, and validation sets in a single folder called “Data”. The material presented makes no mention of the precise sources from which the original photographs were obtained. Instead, the dataset was created by merging data from other sources, and photos are in JPG or PNG format. As a result, the dataset is made out of publicly available Kaggle data, but the actual sources for each image are not indicated in the information provided.The dataset table makes it abundantly evident that the data collection was unequal, possibly resulting in bias towards a certain class during model training. This difficulty can be resolved via a technique known as “data augmentation” rather than by collecting additional data. By intentionally creating additional data points from the ones that already exist, data augmentation increases the dataset’s diversity. This approach facilitates expanding and stabilizing the model’s execution while also guarding against overfitting. During training, pictures may be automatically enhanced using the AutoAugment approach, which enhances the generalization of DL models. We describe the AutoAugment transformation policy, choosing either “v1” or “v2” for augmentation, and build a transformation pipeline that comprises picture resizing, applying horizontal flip, and the selected AutoAugment policy. The training pictures are then loaded from a custom dataset that is kept in a directory structure under the ‘./dataset/train’ folder. The DataLoader is used to load the custom dataset and the ImageFolder class from Torchvision. datasets aid in applying the AutoAugment transformation to each picture. In order to increase performance and generalization, a DL model may be trained on the custom dataset using batches of augmented photos that are provided by the DataLoader during training. Recognizing the difficulty caused by imbalanced class distributions is a commendable observation of a prevalent problem in machine learning. It does not, however, provide a full answer beyond the use of data augmentation strategies. It is critical to investigate improved approaches for dealing with class imbalance in order to obtain more consistent and robust outcomes. While data augmentation may assist in reducing the problem, a wider range of techniques should be examined. These include resampling techniques like oversampling and undersampling, cost-sensitive learning, ensemble methods such as bagging and boosting, anomaly detection, synthetic data generation using techniques such as GANs, transfer learning, and the use of cost-sensitive evaluation metrics.Each of these strategies has distinct benefits and may be customized to the individual properties of the dataset as well as the machine learning task’s goals. Using these sophisticated strategies to address class imbalance is critical for attaining not just improved accuracy but also more resilient and fair machine learning models. Data augmentation is a popular and successful strategy for correcting class imbalance in machine learning problems for a variety of reasons.Algorithm 1: Auto AugmentationStepsExplanationInputOutputStep 1Import the necessary libraries, including albumentations, torchvision, and torch.NoneNoneStep 2Define the function AutoAugment policy defined using AlbumentationsNoneStep 3Load the custom dataset using ImageFolder from torchvision.datasets.Apply the previously defined AutoAugment transformation to the dataset.In the main part of the code:Custom dataset containing images organized by class labelsNoneStep 4Create a DataLoader using torch.utils.data.DataLoader. The DataLoader will handle batching and shuffling of the augmented dataset during training. Define a simple CNN model class SimpleCNN using nn.Module. Set u-Augmented dataset-CNN architecture-Loss function-Optimizer-Hyperparameters- Validation datasetCustom dataset containing images organized by class labelsNoneStep 5Define a simple CNN model class SimpleCNN using nn.Module. Set up the loss function  and optimizer  for training the model. Specify the quantity of training cycles and other hyperparameters like learning rate and batch size.-CNN architecture-Loss function-Optimizer-HyperparametersNoneStep 6Train the model over the course of specified number of epochs:Set the model to training mode by calling Loop through the DataLoader to obtain batches of augmented images and their corresponding labels.To obtain expected outputs, run the model via a forward pass.Calculate the difference between ground truth labels and expected outputs using the defined loss function.Perform a backward pass to compute gradients of the model’s parameters with respect to the loss.Update the model’s parameters using the chosen optimizer and the computed gradients.-Augmented dataset-Model-Loss function-Optimizer-Hyperparameters- Validation dataset-Trained CNN model-Model checkpoints -Training and validation performance metricsStep 7After training is complete, the model is ready for inference and evaluation on new data.-Trained CNN model-Inference dataInference results on new dataTo begin, data augmentation is quite a simple process and does not need significant modifications to the model architecture or hyperparameters. It entails creating new training samples from existing data by using different transformations such as rotation, flipping, cropping, or introducing noise. This artificially expands the minority class, which helps to balance the class distribution. Second, data augmentation is data-driven, requiring no external knowledge or assumptions about the underlying data distribution. It makes use of the existing data to produce more varied instances, which may help the model generalize and make better predictions. The outcomes of augmentation are shown in The MixNet-LD model is a deep neural network architecture created to effectively extract visual representations. MixNet-LD seeks a mix between performance and computational economy, drawing inspiration from the successful Inception and MobileNet models. Mix-depthwise convolutional blocks are used, which combine depthwise separable convolutions with different kernel sizes to enable effective feature extraction at various scales. Furthermore, the feature mixing of blocks with skip connections allows information to move easily across layers, improving gradient propagation. Convolutional layers, batch normalization, and activation functions are used in the model to extract features and account for nonlinearity. For further feature processing, fully linked layers are employed after global average pooling to minimize spatial dimensions. To obtain class probabilities for classification problems, a SoftMax activation function is applied in the ultimate stage as the output layer. Because of its emphasis on computational effectiveness, MixNet-LD is particularly suited for contexts with limited resources, such as mobile devices. The model gains superior performance in numerous computer vision tasks, such as picture classification and object recognition, by being trained on a huge dataset of labeled images. MixNet-LD has to be fine-tuned on a fundus dataset that has been suitably labeled in order to be used for identifying HR in retinography pictures. Below Let x).(((((The notation ((Equation (1) represents the MixDepthwise Convolutional Block. In this equation:Dense blocks, which are differentiated by their dense connectedness, are a fundamental component of the DenseNet design. Each layer inside a dense block is physically linked to every other layer, promoting efficient feature reuse by giving each layer access to the feature maps created by all previous levels. This not only encourages the extraction of fine-grained and high-level features, but it also alleviates the vanishing gradient issue, allowing for the training of extremely deep networks. Furthermore, dense blocks excel at parameter efficiency, lowering the amount of learnable parameters while preserving model expressiveness, making DenseNet an appealing alternative for image classification and other computer vision applications.Dense blocks, which are included into the “MixNet-LD” system, are critical in enhancing the model’s categorization skills. Their extensive connectedness promotes dynamic information sharing across network levels, encouraging feature reuse and improving input data representation. This feature reuse not only allows the model to capture complicated patterns, but it also leads to more a robust gradient flow during training, hence mitigating the vanishing gradient issue that is common in deep neural networks. Furthermore, dense blocks excel in parameter efficiency, allowing the creation of deep models with fewer parameters than traditional designs while maintaining expressive capability.The “MixNet-LD” system stands out as a powerful solution for image classification problems by integrating MixNet’s channel-level information flow with dense blocks’ feature reuse capabilities. It takes use of the synergy between these two architectural aspects to achieve cutting-edge precision while retaining computing economy. Dense blocks, in essence, are a critical innovation that improves the efficacy of the “MixNet-LD” system, providing a viable route for furthering the study of computer vision and picture categorization.The dense block is a standard building block in DenseNet models, comprising two convolutions and a dense connection (Algorithm 2). Let Algorithm 2: Working of the MixNet-DL model for feature map extractionStepsExplanationInputOutputStep 1Input: For each Perform a depthwise convolution on Apply Batch Normalization to the result. Apply ReLU activation to the result.Perform a pointwise convolution on the result with a Concatenate the outputs of all the pointwise convolutions.Output: Concatenated tensor representing the MixDepthwise Convolutional Block output.Define the MixDepthwise Convolutional Block:K (Set of kernel sizes)x (Input feature map)Concatenated tensor representing the MixDepthwise Convolutional Block output.Step 2Input: Save Perform a Apply Batch Normalization to the result.Apply ReLU activation to the result.Perform another  Apply Batch Normalization to the result. Add the result to the shortcut to create the residual connection. Apply ReLU activation to the result. Output: Output tensor representing the Dense block output.Define the Dense block:F (Number of filters)x (Input feature map)Output tensor representing the Dense block output.Step 3Input: inputs (Input tensor to the model) Perform a Apply Batch Normalization to the result. Apply ReLU activation to the result.Perform MaxPooling with a Apply the MixDepthwise Convolutional Block on the result with 1 Apply the MixDepthwise Convolutional Block on the result with 2 Apply the Dense block on the result with 128 filters.Apply the Dense block on the result with 128 filters. Perform a 3 × 3 convolution on the result with 256 filters and ‘same’ padding.Apply Batch Normalization to the result. Apply ReLU activation to the result.Perform Global Average Pooling on the result to reduce spatial dimensions.Perform a Dense layer with 256 units on the result.Apply Batch Normalization to the result.Apply ReLU activation to the result. Perform a Dense layer with the number of classes Output tensor representing the final classification probabilities.Step 4Input shape: Trained and compiled MixNet-DL model ready for classification.The MixNet with Dense blocks can be represented as follows:Automatically recognizing lung diseases from lung X-ray and CT scan images presents issues for CAD systems. The MixNet architecture is used to create the MixNet-LD method for effective picture classification in order to solve this. a(z), the entire dense block functions by analyzing the actual output. The layers are working hard to learn the residual, DB(z), according to a close inspection of a(z)), the layers in a dense network learn about the dense DB(z).To improve its performance on job The linear SVM machine learning classifier, with a train–test splitting ratio of 75% to 25%, is used for the classification job. This decision was made because of how well linear SVM performs overall and how well it handles tiny datasets. SVM is a classification approach that is well known for being superior to alternative classifiers and is commonly employed to solve practical issues. Instead of using conventional DL or machine learning classifiers, the authors chose to create a depth-wise separable CNN for computer vision or image classification problems. However, the linear SVM classifier was chosen for the current study because it is excellent at handling tiny datasets although not yet achieving well in high-dimensional environments. While using a linear layer in the context of an SVM classifier is unusual, it most likely serves a function within the larger architecture or experimental setting. SVMs have generally been linked with linear decision boundaries and margin optimization; nevertheless, this representation may use neural network architectures and deep learning frameworks for training and integration with other components. However, for a complete understanding of the technique, further information about why this specific representation was selected and how it affects the model’s performance, training process, or relationship with other architectural parts is required. Clarity in this regard would improve the paper’s contribution by revealing the logic behind this atypical strategy and its consequences for the research’s aims and findings.Given that the task required binary categorization, this choice made sense. In the process to boost the efficiency of their approach and identify the most effective hyperplane to distinguish between abnormal and healthy cells in retinal images, the authors also used linear SVM. Typically, an LSVM takes a vector c is the offset. Weig and c are real-valued parameters that were both learnt during training and are a part of the real numbers Algorithm 3: Proposed LSVM classifier.StepsExplanationInputOutputCategorization of normal and abnormal sections1Regularize Primarily, the classifier and kernel2a. The training procedure of LSVM is finished. using obtained attributes b. For the production of the hyperplane, use Equation (6).Creation of LSVM3testing samplesThe class label is assigned forWeig stands for the weight in Equation (9), while A collection of 13,313 lung pictures, including both high-resolution normal and diseased images, was used to train the MixNet-DL model. These lung photos were obtained from credible Pakistani hospitals (Pak-Lungs) and a number of reliable online sources. All 13,313 images were downsized to pixels for feature extraction and classification activities. Utilizing MixNet and leftover building blocks, the MixNet-DL system underwent training for 100 epochs. The best model was found in the 30th century and achieved an excellent F1-score of 0.97. The accuracy (ACC), specificity (SP), and sensitivity (SE) values for the proposed MixNet-DL system were determined by statistical analysis. Then, these measurements were contrasted with those from other systems. The MixNet-LD system was designed and built on a computer with an HP-i7 CPU with 8 cores, 32 GB of RAM, and a 2 GB Gigabyte NVIDIA GPU. Windows 11 Professional 64-bit is the operating system installed on this machine.In order to estimate the execution of the DL models VGG16, VGG19, InceptionV3, ResNet, Xception, and MobileNet with the proposed MixNet-DL system, we ran an experiment in this study. Notably, the same number of epochs was used to train each of these DL models. Two identical deep neural networks were trained after the top network was determined based on validation accuracy. In this study, we use a dataset called “lung disease dataset (four types)” which we downloaded from a reliable online source , to evalIn this investigation, we estimated the effectiveness of our suggested. MixNet-DL method using the Pak-Lungs dataset. First, we evaluated the loss function and the representation’s operation on the training and validation sets using the Pak-Lungs dataset. The confusion matrix and training and validation accuracy of the MixNet-DL model when trained on this dataset are shown graphically in In this paper, we test the efficacy of our proposed MixNet-LD technique using a novel dataset named Pak-Lungs (cancer) and Chest CT-Scan images dataset , which wMobileNet  posts imHowever, the proposed MixNet-LD model stands out with remarkable superiority, recording near-perfect scores across all metrics. The model demonstrates a sensitivity and recall of 0.99, specificity of 0.985, an F1-score of 0.988, and an astonishing accuracy of 0.99. This significant outperformance of MixNet-LD underscores its advanced capabilities and effectiveness in accurately classifying diverse lung conditions, solidifying its position as a leading tool in the field, as per the presented data.Several lung diseases, including pneumonia, tuberculosis (TB), COVID-19, and lung cancer, may now be detected and classified thanks to substantial breakthroughs achieved by DL in medical image analysis. With high morbidity and mortality rates, these illnesses represent serious problems for public health internationally. For rapid treatment and better patient outcomes, diagnosing and classifying these illnesses as early as possible is essential. CNNs have demonstrated excellent performance in diagnosing lung illnesses in recent years. DL models for analyzing chest X-rays have been developed partly due to research that introduced the ChestX-ray8 dataset. The basis for identifying pneumonia and other thoracic illnesses has been established using this dataset in conjunction with weakly supervised classification and localization approaches. For effective picture classification, multi-scale dense networks utilizing deep CNN architectures should be introduced. By extracting hierarchical characteristics, this method has proven effective in reliably detecting lung illnesses.The COVID-19 pandemic’s advent presented a necessity for accurate and speedy detection. An artificial intelligence system can distinguish COVID-19 from community-acquired pneumonia in chest CT images. This method helps identify COVID-19 patients by utilizing DL techniques, which improve disease treatment and control. A DL model for automatic pulmonary TB categorization on chest radiography has been tested. This approach enabled rapid screening and diagnosis by precisely identifying TB-related abnormalities. DL’s application of the COVID-19 diagnosis has drawn a lot of interest for transfer learning and CNNs in placing COVID-19 in X-ray pictures. This method showed how DL may help radiologists and other healthcare professionals swiftly identify COVID-19 patients. Focusing on interstitial lung illnesses, we developed a deep CNN for lung pattern classification. In addition, we developed and evaluated an AI system for COVID-19 diagnosis that correctly identified COVID-19 patients from imaging data using DL techniques.Along with infectious diseases, the diagnostic potential of DL models for lung cancer has also been thoroughly investigated. An image-based DL system can identify medical diagnoses, such as lung cancer. This model illustrated how DL may be used to detect the planned MixNet-LD system for the automatic identification and classification of lung diseases. The lungs are essential for the respiratory system, and they must function properly for the body to exchange oxygen and carbon dioxide. However, several illnesses can impact the lungs and have adverse health effects. Since it might be challenging to identify and extract the lesion characteristics connected to lung problems, early and precise diagnosis of lung diseases is crucial for prompt and effective therapy. To solve this problem, the MixNet-LD system combines the strength of MixNet, a particular pre-trained model, with dense blocks. By minimizing noise and emphasizing abnormalities, the suggested pre-processing technique utilizing Grad-Cam improves the quality of pictures of lung illness, further enhancing the classification’s performance. Data augmentation techniques are also used to correct the dataset’s class imbalance and avoid overfitting, strengthening, and improving the MixNet-LD model. The creation of the Pak-Lungs dataset, which consists of 6000 photos of lung diseases gathered from reliable online sources and Pakistani hospitals, is a significant addition to the study. The MixNet-LD model successfully handled various lung illness cases thanks to this sizable dataset, achieving a fantastic accuracy of 98.5% on the challenging lung disease dataset. Further demonstrating the superiority of the MixNet-LD system, which exceeds previous techniques with a substantially higher accuracy of 95%, is the comparison with state-of-the-art methods in medical image processing. As it offers a trustworthy and automated way of correctly diagnosing normal COVID-19, pneumonia, tuberculosis, and lung cancer illnesses, the system’s performance can potentially have real-world ramifications in medical diagnosis and therapy. The MixNet-LD system successfully combines MixNet with dense blocks and uses data augmentation methods. While MixNet’s pre-trained model offers the benefit of transfer learning, allowing the system to learn from past knowledge and attain cutting-edge performance, the dense blocks will enable the extraction of critical characteristics from the pictures. Using data augmentation approaches makes the model more flexible and able to handle various lung illness situations while lowering the danger of overfitting.Overall, the MixNet-LD method better presented state-of-the-art methods and performed well, making it an essential tool in medical image analysis. It can also handle a variety of lung disease scenarios. The MixNet-LD system can dramatically boost patient outcomes and healthcare management by delivering accurate and timely diagnostics of lung illnesses. Further investigation can look at the possibilities of the MixNet-LD system in various clinical contexts and the applicability of the technology to other medical image analysis jobs. The system’s breadth and influence in medical imaging may be increased by ongoing improvement and optimization, ultimately leading to more efficient medical image processing in clinical settings. The MixNet-LD system has far-reaching and varied implications for medical image processing in clinical settings. MixNet-LD has the potential to transform how medical pictures are evaluated, interpreted, and used in healthcare by using the power of deep learning. Its significance in improving diagnostic accuracy is especially notable since it may aid healthcare workers in accurately detecting disease disorders. This results in not just early interventions but also more personalized treatment strategies, eventually improving patient outcomes. Furthermore, MixNet-LD improves operational efficiency in clinical processes. It can scan and triage a large amount of medical pictures quickly, prioritizing instances that need urgent treatment. This is particularly useful in resource-constrained healthcare settings where rapid decision making is crucial. MixNet-LD may also relieve the strain on radiologists and medical personnel by automating typical image analysis operations, enabling them to concentrate on complicated situations and provide more complete patient care. The model’s consistency in picture interpretation, as well as its potential for telemedicine applications, highlight its practical importance in current healthcare delivery. However, like with any new technology in clinical practice, extensive validation, ethical concerns, and continual refinement are required to guarantee MixNet-LD’s responsible and successful incorporation into the medical environment.The research around the MixNet-LD system has the potential to profoundly transform real-world healthcare procedures. This technology promises to improve diagnosis accuracy, expedite resource allocation, and minimize the strain of healthcare personnel by using deep learning capabilities. Patients will benefit from earlier and more exact diagnoses, which will lead to earlier treatments and better treatment results. Furthermore, MixNet-LD’s function in telemedicine and clinical decision support may broaden access to healthcare and provide vital insights for personalized treatment. This research has the potential to expedite the training of healthcare professionals and accelerate the pace of discovery, ultimately contributing to a patient-centric healthcare ecosystem that is more efficient, effective, and responsive to the evolving needs of both patients and providers. However, careful consideration of ethical, privacy, and security issues will be required to enable MixNet-LD’s responsible and successful incorporation into real-world healthcare procedures.(1)At the heart of the conclusion lies the remarkable performance of the MixNet-LD system in precisely categorizing the severity of lung diseases. The approach achieves a level of accuracy and learning capabilities that position it at the forefront of advancements in the field. This achievement underlines the potential of the approach to significantly enhance the accuracy of classification models within the domain of medical image analysis, a critical aspect of modern healthcare diagnostics.(2)Integral to the success of the MixNet-LD approach are the adept utilization of data augmentation strategies and pre-processing techniques, such as Grad-Cam. These methods not only improve the quality of the input data but also improve the classification performance of the model. Furthermore, the paper underscores the importance of addressing the inherent challenge of unbalanced class distributions in the dataset. By incorporating dense blocks and data augmentation techniques, the MixNet-LD approach effectively navigates this challenge, ensuring that the model’s performance remains robust across all severity categories.(3)A notable aspect of the MixNet-LD system is its ability to achieve remarkable results without sacrificing model size and complexity. This pragmatic approach ensures that the model’s deployment remains feasible in real-world clinical settings, where resource constraints and practical considerations are paramount. By striking a balance between performance and complexity, the MixNet-LD approach attains a degree of practicality that enhances its potential for widespread adoption and implementation.(4)The generalizability of the MixNet-LD approach is another key takeaway from the conclusion. The approach is put to the test across a diverse array of datasets, including both a publicly available and novel private dataset known as Pak-Lungs. This comprehensive evaluation underscores the adaptability and effectiveness of the MixNet-LD approach, suggesting its potential applicability in various clinical contexts.(5)By advancing the accuracy of automated disease severity classification, the MixNet-LD system adds a noteworthy contribution to the broader field of medical image processing. This contribution resonates beyond the confines of the paper, suggesting promising directions for the development of more effective strategies for medical diagnosis and treatment.The paper concludes by highlighting the significant strides made by the MixNet-LD approach in the realm of automated lung disease severity classification. It accentuates the critical need for accurately identifying and categorizing the severity of lung diseases, as this information forms the cornerstone of effective medical diagnosis and treatment strategies. The MixNet-LD methodology, founded upon an enhanced pre-trained MixNet model, emerges as a promising solution poised to tackle this complex challenge:Overall, the advantage solidifies the MixNet-LD approach as a powerful tool in automating the classification of lung disease severity, with far-reaching implications for the advancement of medical image analysis within clinical settings.Future research could explore the integration of multiple modalities of medical imaging data, such as CT scans and X-rays, into the MixNet-LD approach. Combining information from diverse sources could enhance the model’s ability to accurately classify disease severity and provide a more comprehensive diagnostic tool.Developing methods to interpret and explain the decisions made by the MixNet-LD model could enhance its clinical utility. Research could focus on creating visualization techniques that highlight the regions of interest in medical images that contribute to the severity classification, aiding medical professionals in understanding and trusting the model’s predictions.While MixNet-LD has shown promise in lung disease severity classification, similar approaches could be investigated for other medical conditions. Researchers could adapt and extend the methodology to automate severity assessment in diseases affecting different organs or systems, contributing to a wider range of clinical applications.Exploring techniques to deploy the MixNet-LD approach in real-time diagnosis could revolutionize patient care. Developing optimized models that can provide severity assessments quickly and accurately would be valuable for guiding immediate medical decisions and interventions.Conducting large-scale clinical studies to validate the MixNet-LD approach’s performance on diverse patient populations would bolster its credibility and real-world applicability. Collaborations with medical institutions could provide access to extensive datasets, allowing researchers to assess the model’s performance across various demographics.Integrating the MixNet-LD approach into clinical decision support systems could provide physicians with an additional layer of information when making treatment recommendations. The model’s predictions could be used as a complementary tool to aid doctors in diagnosing and planning patient care.Developing mechanisms for online learning and continuous model improvement could allow the MixNet-LD approach to adapt to emerging patterns and changes in lung disease severity. The model could be trained on newly available data to ensure its relevance and accuracy over time.Investigating the potential benefits of ensemble methods, where multiple models are combined, could enhance the overall performance of the MixNet-LD approach. Combining different architectures or training strategies could lead to improved accuracy and robustness.In addition to other DL approaches, NASNet, MobileNet, and EfficientNet model analysis of fresh datasets may be performed in the future.Descriptions of potential future works that could build upon the achievements of the MixNet-LD approach in the field of automated lung disease severity classification are given as follows:In summary, the future of research in this area holds promising opportunities for further innovation and impact. Continued exploration and refinement of the MixNet-LD approach, along with addressing challenges and expanding its scope, could lead to substantial advancements in automated lung disease severity classification and broader applications in medical image analysis.In this study, a deep learning-based architecture was developed and utilized to classify chest illnesses into several categories, including normal, COVID-19, pneumonia, tuberculosis, and lung cancer. The architecture was trained and evaluated using chest X-ray images. The MixNet-LD system, as detailed in the paper, emerges as a pioneering solution in the precise classification of diverse lung diseases, achieving an exceptional 98.5% accuracy rate. The system leverages a robust pre-processing strategy, employing Grad-Cam and data augmentation to enhance its performance and avoid overfitting, even with imbalanced class distributions. The use of dense blocks further refines its classification capabilities across various lung disorders. Tested on multiple datasets, including a novel “Pak-Lungs” dataset, the model’s consistent high performance underscores its reliability and effectiveness. The innovative design of the MixNet-LD system, featuring additional layers and effective feature extraction using CNN, marks a significant advancement in medical image processing. This research highlights the potential of the MixNet-LD system to greatly improve the accuracy of lung disease classification, offering a promising tool for enhanced clinical diagnosis and treatment strategies.


Phenotypes of Myasthenia gravis (MG), the second special topic has been published. This special topic, including 3 original research articles and 1 brief research report, contributes to further understanding and assessment of MG phenotypes. Although fewer publications were included on this topic, the research progressed into a deeper and more comprehensive understanding and application of phenotypic analysis. Here, we briefly discuss these studies and present our reflections on a preliminary roadmap for MG phenotype studies.Following the first special topic in the Liu et al.). The same group previously found that diabetes mellitus aggravated the aberrant humoral immunity in MG patients by promoting differentiation and activation of circulating follicular helper T cells , and the risk of MG. One hundred and eighteen hospitalized MG patients were recruited for different comparisons with four datasets of different control groups, including the general population and inpatients hospitalized for non-MG and non-diabetes diseases. A conditional logistic regression analysis was used to test the risk of MG associated with T2DM. The risk of MG was found to be significantly associated with T2DM after a hierarchical analysis considering the onset of T2DM before or after the onset of MG, and other autoimmune diseases . The limitation of this brief report is the sample size, being a single-center study. The relationship between myasthenic crisis and summer and thymic hyperplasia waits for confirmation from other patient cohorts.In a retrospective cohort of generalized MG patients who were consecutively recruited during the last 10 years, myasthenic crises were reported in a non-selected cohort from Istanbul, Turkey. Some interesting findings were noted. Thymic hyperplasia was found associated with a decreased risk of crisis. Furthermore, normal thymus was found in patients both with and without crisis in a similar proportion, which accounted for about 40% in both groups. In contrast to the expectations of the authors, most crises occurred in the summer, whereas more frequent and severe infections which may trigger myasthenic crisis occur in autumn and wintertime. The authors paid attention to COVID-19 infections during the summer of the pandemic. However, no patients developed a crisis after COVID-19 infection .A real-world evaluation of a smartphone-based research platform in characterizing precise symptom development during MG exacerbations was conducted in US MG patients who were representative of a moderate to severe MG phenotype, with frequent exacerbations, high symptom burden, and multiple comorbidities. There was a significant difference in the median MG Activities of Daily Living (MG-ADL) scores during self-reported exacerbation and non-exacerbation periods. Concordance between self-reported MG-ADL scores and exacerbation status was demonstrated. The dynamic changes in day-to-day symptom characteristics and severity, the daily step-counts as a measure of physical activity, and the general clinical characteristics of the patients were identified as essential variables to predict and evaluate the onset of MG exacerbations. Using all collected information, unsupervised machine-learning methods identified unique clusters of exacerbation subtypes. This smartphone-based research platform seemed to provide a convenient and accurate tool for characterizing symptoms in relation to MG exacerbations, and represents a paradigm in the patient-centered artificial intelligence research in the phenotypes of MG, which should also have high significance for daily clinical practice . This study adds evidence regarding gut microbiota in the development of MG. More studies on the relationship between microbiota and the immune status of MG are awaited.In a gut microbiota and metabolite study, fecal samples were collected from 11 newly diagnosed untreated MG patients and 11 age- and sex-matched healthy controls. The microbial community richness and diversity were significantly lower in the MG group compared to the control group. Microbiota composition analysis revealed significant differences between the MG and control groups at phylum, family, and genus levels. A substantial decrease in the abundance of the genus Faecalibacterium was found in the MG group. Fecal metabolome analysis identified three up-regulated metabolites involved in amino acid metabolism and one up-regulated metabolite involved in lipid metabolism. A positive association was found between Faecalibacterium abundance and creatinine levels  and the accurate and convenient data collection methodology and smart tools.D. Optimize basic data elements and derived data elements  that reflect the immune status of MG  , 3 and tE. Optimize data elements for comorbidities and MG aggravating factors.F. Optimize data elements for treatment intensity, compliance, and adverse effects.G. Optimize data elements for psychological factors, emotional factors, and stressful life events.H. Optimize data elements for special subgroups such as those with pregnancy, breastfeeding, infectious diseases, and malignanies.I. Optimize data elements for pediatric patients, including growth and development.J. Optimize data elements for thymus pathology and thymectomy, and related health consequences.A. Optimize data elements for autoantibodies and disease-inducing antibodies of MG, i.e., antibodies against various epitopes of AChR, MuSK, and Lrp4 . The repB. Optimize data elements for newly explored autoantibodies, especially regarding antigenic epitope, detection methods, method of processing raw data to form reported values, and the relationship to established antibodies or results with traditional methods for antibody measurement.C. Optimize data elements for essential biomarkers of immunophenotypes of MG, including molecular and cytologic patterns of innate and adaptive immunity.D. Assess the time-locked relationship between clinical data and molecular and cytologic biomarkers in monitoring the disease course and immune status of MG.E. Optimize data elements for genomics biomarkers associated with the susceptibility and immune status of MG.F. Optimize data elements for proteomics and metabolomics biomarkers associated with immune status.G. Optimize data elements for microbiota associated with the susceptibility and immune status of MG.A. Use patient-centered outcome measures, and actively integrate patient-involved study designs with traditional physician-led study designs .B. Acquire accurate data through an optimal design of baseline and outcome measures, and operational quality control .C. Develop performance measures for treat-to-target management in MG and assessment of potential factors for the refractory status.D. Examine various treatment strategies and individualized pathways for the treatment of MG, especially the timing and combination of various interventions and their sequence.E. Develop and evaluate more accurate assessing and predicting methods for short-term and long-term therapeutic efficacy.F. Optimize data elements for socio-economic analysis for MG interventions in MG cohorts with different socio-economic background. Implement decision analysis, and explore the decision nodes, also with the inclusion of nest cost-benefit analysis.G. Provide medical records including but not limited to the minimal information at each follow-up visit, including symptoms and severity, assessment of exacerbation, post-intervention status, detailed scores of each item of the commonly-used self-reported and physician-rated scales, immunotherapies and relevant duration between each follow-up, levels of disease-inducing antibody at irregular and/or prespecified intervals , and major comorbidities and relevant management. The above information helps to provide the necessary information when the patients visit different physicians and to alert physicians and patients to some of their changes.H. Develop the performance and quality control measures for the clinical classification, scale evaluation, post-intervention state and other standardized evaluation methods.Optimal MG treatment must adapt standard guideline recommendations to actual decisions for each individual patient. Modern medicine needs to be personalized but should at the same time be evidence-based, preferentially based on well-controlled scientific studies. However, such studies include patients with a variety of different characteristics. A key element for successfully combining general treatment standards with individual success is a precise evaluation of MG phenotype. Treatment guidelines should be adapted to phenotypically well-defined MG subgroups. Each patient should then be carefully evaluated regarding clinical and non-clinical parameters, leading to a precise phenotype assessment. The series of articles that we have edited in Frontiers of Neurology have illustrated the importance of such precise MG phenotyping, the consequences of having a specific MG phenotype, and the need for more research into defining phenotypic MG subgroups that are relevant for therapeutic response and safety.HFL: Conceptualization, Writing – original draft, Writing – review & editing. NEG: Conceptualization, Writing – original draft, Writing – review & editing. QX: Conceptualization, Writing – review & editing. FG: Conceptualization, Writing – review & editing.