Rapid Detection Research Articles

Use of McIsaac Scoring and Rapid Streptococcal Antigen Detection Test for Diagnosis of Group A Streptococcal Pharyngitis in Children

Use of McIsaac Scoring and Rapid Streptococcal Antigen Detection Test for Diagnosis of Group A Streptococcal Pharyngitis in Children

Combining NIR spectroscopy with chemometrics for discriminating naturally ripened banana and calcium carbide ripened banana

Calcium carbide is prohibited as a fruit ripening agent in many countries due to its harmful effects. Current methods for detecting calcium carbide in fruit involve time-consuming and destructive chemical analysis techniques, necessitating the need for non-destructive and rapid detection techniques. This study combined near infrared (NIR) spectroscopy with chemometrics to detect two banana varieties ripened with calcium carbide in different forms when they are peeled or unpeeled. Sixteen linear discriminant analysis (LDA) models were developed with high average classification accuracies for classifying banana based on the mode used to ripen banana, type of carbide treatment and the duration of soaking banana in carbide solution. Banana colour was predicted with partial least squared regression (PLSR) models with R2CV > 0.74, RMSECV and <5.4 and RPD close to 3. NIR coupled with chemometrics has good potential as a technique for detecting carbide ripened banana even if the banana is peeled or not.

Novel metal–organic framework biosensing platform for detection of COVID-19 RNA

The latest pandemic resulting from the novel SARS-CoV-2 coronavirus has significantly affected public health, the worldwide economy, and social life. Metal organic frameworks (MOFs) are currently being implemented in biosensors for rapid and accurate detection of viruses thanks to their exceptional properties. This research aims to develop a Zeolitic Imidazolate Framework-8 (ZIF-8) based fluorescent biosensor for facile and rapid COVID-19 RNA sequence detection. ZIF-8 was characterized using several tests, such as FT-IR, TGA, and PXRD, to examine the MOF’s crystalline structure and thermal stability. The results demonstrated high crystallinity and thermal stability up to a temperature of 550 °C. The experimental study showed that ZIF-8 is an excellent fluorescence quencher, with 78.39% quenching efficiency. Analyzing the adsorption mechanism of probe DNA into ZIF-8 revealed that they can form electrostatic and π–π stacking interactions, forming a P-DNA@ZIF-8 complex and that PET is more dominant than FRET in the quenching mechanism. This ZIF-8 biosensing platform showed high sensitivity towards COVID-19 RNA with an ultra-low limit of detection of 6.24 pM, a rapid detection time of 8 min, and high selectivity to COVID-19 RNA. Indeed, ZIF-8 experienced much lower fluorescence recovery when tested on two mismatched RNAs. The experimental results show the potential use of ZIF-8 as a novel biosensor for a rapid and sensitive COVID-19 diagnosis.

A fluorescent sensor for rapid and quantitative aquatic bacteria detection based on bacterial reactive oxygen species using Ag@carbon dots composites.

A novel fluorescent sensor based on silver nanoparticle-carbon dot composites (Ag@CDs) has been developed for the rapid and quantitative detection of aquatic bacteria. The sensor operates on the principle of plasmon-enhanced resonance energy transfer, where the fluorescence of CDs is quenched by Ag nanoparticles and restored upon bacterial interaction due to the generation of reactive oxygen species. The Ag@CDs exhibit a linear response to bacterial concentration over the range 7 × 104 ~ 4 × 107 CFU·mL-1, with a low detection limit of 4 × 104 CFU·mL-1. The fluorescence recovery is rapid, reaching maximum intensity within 5 min. The method demonstrates high selectivity, with minimal interference from common ions and compounds found in municipal and industrial wastewater. The Ag@CDs-based 96-well plate assay for quantitative measurement of bacteria was developed. The assay's performance was further validated through the analysis of real water samples, showing a recovery of 94.0 ~ 102% for domestic wastewater and 97.6 ~ 106% for industrial wastewater. Also, Ag@CDs-based test strips assay for semi-quantitation were developed for rapid in-field aquatic bacteria detection. Ag@CDs can be conveniently integrated into 96-well plates and test strips, providing rapid on-site aquatic bacteria testing.

Reference-based image super-resolution of hyperspectral and red-green-blue image for determination of wheat kernel quality using deep learning networks

In the process of cultivation and harvest, wheat kernel quality is highly susceptible to various factors, such as disease, mildew, atrophy and impurities, and detection of kernel quality is essential to avoid hazard proliferation, facilitate product grading, and ensure food safety. Possessing abundant image and spectral characteristics, hyperspectral imaging (HSI) has gained impressive achievements in kernel quality analysis, but its low spatial resolution limits its detection accuracy. In this study, reference-based image super-resolution (RefSR) of HSI and Red-Green-Blue image was adopted to improve resolution to determine wheat kernel quality using deep learning networks. Firstly, RefSR was conducted by the improved transformer network with dual-branch feature extraction and weighted fusion operation and achieved excellent RefSR with significant resolution improvement, peak signal to noise ratio of 35.521 and structural similarity index of 0.97, outweighing the existing state-of-the-art networks. Then, the reflectance images (RIs) of effective wavelengths (EWs) from generated HSI images were combined with the residual network with a spatial, channel attention and multi-scale residual to determine wheat kernel quality. Precise analysis was achieved with the accuracy in calibration, validation and prediction sets of 100.00%, 95.26% and 92.78%. RefSR provides a novel and efficient approach for obtaining HSI images of high spatial resolution and facilitates the application of HSI in analysis of crop kernels. RIs of several sporadic EWs can be easily acquired and processed, achieving field and rapid kernel detection. Therefore, the proposed method furnishes the efficient, accurate and applicable determination of wheat kernel quality.

Research on in-vivo electron paramagnetic resonance spectrum classification and radiation dose prediction based on machine learning

The in-vivo electron paramagnetic resonance (EPR) method can be used for on-site, rapid, and non-invasive detection of radiation dose to casualties after nuclear and radiation emergencies. For in-vivo EPR spectrum analysis, manual labeling of peaks and calculation of signal intensity are often used, which have problems such as large workload and interference by subjective factors. In this study, a method for automatic classification and identification of in-vivo EPR spectra was established using support vector machine (SVM) technology, which can in-batch and automatically identify and screen out invalid spectra due to vibration and dental surface water interference during in-vivo EPR measurements. In this study, a spectrum analysis method based on genetic algorithm optimization neural network (GA-BPNN) was established, which can automatically identify the radiation-induced signals in in-vivo EPR spectra and predict the radiation doses received by the injured. The experimental results showed that the SVM and GA-BPNN spectrum processing methods established in this study could effectively accomplish the automatic spectra classification and radiation dose prediction, and could meet the needs of dose assessment in nuclear emergency. This study explored the application of machine learning methods in EPR spectrum processing, improved the intelligence level of EPR spectrum processing, and would help to enhance the efficiency of mass EPR spectra processing.

Rapid and Efficient Dual Detection Of Zn2+ Ions and Oxytetracycline Hydrochloride Using a Responsive Fluorescent "On-Off" Sensor Based on Simple Salen-Type Schiff Base Ligand.

This research has progressed to an effective detection chemosensor of zinc, aluminum ions and oxytetracycline hydrochloride antibiotic based on the fluorescence technique. A straightforward method utilizing microwave irradiation was employed to synthesize the salen-type Schiff base ligand N,N'-bis(salicylaldehyde)4,5-dichloro-1,2-phenylenediamine (H2I), providing a good 70 % yield. In ethanol, the H2I sensor demonstrated remarkable rapidity, selectivity, and sensitivity in detecting zinc ions. The fluorescence spectrum exhibited a 44-fold substantial enhancement at 522 nm and achieved a low limit of detection (LOD) of 1.47 μM. Furthermore, the H2I probe's emission intensity increased by 124 times when compared to the ligand's ability to detect Al3+ ions at 494 nm with a LOD value of 7.4 μM. Additional research was done using the H2I probe's effective Zn2+ detection capability. The ability to recognize zinc ions in different real water samples demonstrated a recovery rate of 98.67 % to 103.31 %. Interestingly, a naked-eye visible fluorescence color of H2I solution impregnated filter papers turned colorless into yell ow under UV irradiation by adding Zn2+ ions, renders it suitable for developing a practical zinc ion detection kit test. In particular, the I-Zn2+ complex effectively quenched the fluorescence toward oxytetracycline hydrochloride (OTC) with a LOD value of 1.49×10-2 μM in DMSO:H2O (6 : 4, v/v). This is a novel and effective procedure for sensing OTC antibiotic by the I-Zn2+ complex. These findings hold immense potential for the development of dual fluorescent probes, thereby enhancing sensitivity and specificity in identifying metal ions and antibiotics in a wide range of applications.

Use of point-of-care tests in pharyngotonsillitis – a registry-based study in primary health care

Background Point-of-care (POC) tests, including C-reactive protein (CRP) tests and rapid antigen detection tests (RADT) for group A streptococci (GAS), are widely used in Swedish primary health care (PHC). This study quantifies their use in pharyngotonsillitis and explore their association with antibiotic prescribing. Material and methods Retrospective data from 2012–2016 in Region Kronoberg, Sweden, included all PHC visits with a pharyngotonsillitis diagnosis. Patient characteristics, test usage and antibiotic prescriptions were linked by visit date and personal identification number. Descriptive statistics were used for POC test analysis. Logistic regression assessed the association between CRP levels and antibiotic prescribing. Results Of 24,237 visits, 68% included RADT and 36% included a CRP test, with 89% of CRP tests performed alongside RADT. CRP testing was more frequent in patients with negative (56%) than positive RADTs (42%) (p < .001). Overall, 66% of RADTs were positive. Median CRP levels were 23 mg/l for positive RADT and 31 mg/l for negative RADT (p < .001). Antibiotics were prescribed for 95% of positive RADTs and 43% of negative RADTs (p < .001). In patients with negative RADTs, CRP testing was associated with higher antibiotic prescribing (57%) compared to no CRP testing (26%) (p < .001). Among these patients, CRP levels were associated with prescribing (aOR 1.032; 95% CI 1.029–1.035; p < .001), with 50% of prescriptions occuring at CRP levels ≤ 20 mg/l. Conclusion The use of RADTs and the proportion of positive test were higher than expected, indicating inappropriate use and diagnostic bias. CRP testing, contrary to guidelines, was common and associated with increased antibiotic prescribing.

Functional Nucleic Acids as Bacterial Biosensors: A Focus on Clostridioides difficile Infection

Clostridioides difficile (CD) is a Gram‐positive, anaerobic, and spore‐forming bacillus that colonizes the human gut and causes a range of diseases, such as pseudomembranous colitis and antibiotic‐associated diarrhea, that are generally known as CD infection (CDI). Rapid and accurate detection of CDI with high sensitivity and specificity is crucial for patient treatment, infection control, and epidemiological monitoring. Current diagnostic methods for CDI have several limitations, such as high cost, long turnaround time, suboptimal sensitivity, and the need for specialized equipment. Hence, novel detection methods that can overcome these limitations are needed. Functional nucleic acids (FNAs) are a promising class of molecular recognition element (MRE) that can be incorporated into biosensors for detecting infectious pathogens. Several FNAs have been developed for detecting CD. In this review, an overview of CD, CDI, and current diagnostic methods for CDI and their drawbacks are provided. Furthermore, the design principles and working mechanisms of FNAs as well as their applications for the detection of pathogenic bacteria, including CD, are discussed. The potential for developing point‐of‐care paper sensors using currently available CD‐selective FNAs is also highlighted.

Mechanism of TMB Discoloration Catalyzed by Layered CoNi@CN Nanozymes: Application Based on Smart Phone for Resorcinol Detection

Comprehensive SummaryReal‐time on‐site monitoring of resorcinol (RS) concentrations is crucial for detecting hazardous levels, enabling prompt response measures to mitigate potential environmental and health risks. In this study, we developed an innovative method using CoNi@CN‐2 nanozymes to activate peroxymonosulfate (PMS) for oxidizing 3,3',5,5'‐tetramethylbenzidine (TMB). Our results show that the formation of Ni2+ through the oxidation of Ni0 on the CoNi@CN‐2 surface significantly enhances the electron‐donating capacity of Co0. The catalytic reaction of TMB is mediated by redox active species (SO4•−, •O2−, •OH and 1O2). RS drives colorimetry by transferring electrons to the benzene ring and specific nitrogen atoms in ox‐TMB, reducing ox‐TMB to TMB. Furthermore, the colorimetric assay shows a robust linear correlation between RS concentration and absorbance (Abs), described by Abs = –0.44[RS] + 0.886 (0—200 μmol/L, R2 = 0.983). Also, we introduce a novel smartphone‐integrated autonomous detection software that can analyze RS concentration and grayscale values (GSV), yielding GSV = 0.327[RS] + 63.601 (0—200 μmol/L, R2 = 0.990) with a detection limit of 5.29 μmol/L. Additionally, excess PMS leads to ROS attacking specific sites in ox‐TMB, forming secondary oxidation products. This study has enabled rapid and accurate detection of RS, making a significant contribution to environmental safety and protection.

Estimation of the soluble solid content of citrus based on the fractional-order derivative and optimal band combination algorithm.

The soluble solid content (SSC) is a primary characteristic index for evaluating the internal quality of citrus fruits. The development of rapid and nondestructive SSC detection techniques can help address the current issues of postharvest quality grading in China's citrus industry. In this study, a total of 261 experimental samples, including 70 Murcott, 91 Clementine, and 100 Navel orange, were divided into prediction and validation sets in a 7:3 ratio. After obtaining the reflection spectra and SSCs, SNV-FOD (Standard Normal Variate-Fractional-Order Derivative) was used to process the spectra, and the optimal band combination algorithm was introduced to select SSC-sensitive bands. Then, the obtained optimal dual-band combination was input into eight regression models for comparison, and the best performing models stacked ensemble models was selected. Finally, the H-ELR (HyperOpt-optimized ensemble learning regression) model, optimized using a Bayesian function, was applied for the effective estimation of SSC for three common citrus varieties in Guangxi, Murcott, Clementine, and Navel oranges. The results show that (1) the SNV-FOD preprocessing method proposed in this study improved the correlation coefficient with the SSC from 0.546 to 0.836 compared to that of the original spectrum, (2) the optimal dual-band combination (969 and 1069nm) constructed by integrating the differential index and 1.2-order derivative yielded the most accurate results (RPD=2.13), and (3) the H-ELR model, based on HyperOpt optimization, achieved good estimated performance (RPD=2.46). PRACTICAL APPLICATION: This research contributes to the development of practical SSC prediction instruments with excellent universality and ease of application.

Colon polyp detection based on multi-scale and multi-level feature fusion and lightweight convolutional neural network

Early diagnosis and treatment of colorectal polyps are crucial for preventing colorectal cancer. This paper proposes a lightweight convolutional neural network for the automatic detection and auxiliary diagnosis of colorectal polyps. Initially, a 53-layer convolutional backbone network is used, incorporating a spatial pyramid pooling module to achieve feature extraction with different receptive field sizes. Subsequently, a feature pyramid network is employed to perform cross-scale fusion of feature maps from the backbone network. A spatial attention module is utilized to enhance the perception of polyp image boundaries and details. Further, a positional pattern attention module is used to automatically mine and integrate key features across different levels of feature maps, achieving rapid, efficient, and accurate automatic detection of colorectal polyps. The proposed model is evaluated on a clinical dataset, achieving an accuracy of 0.9982, recall of 0.9988, F1 score of 0.9984, and mean average precision (mAP) of 0.9953 at an intersection over union (IOU) threshold of 0.5, with a frame rate of 74 frames per second and a parameter count of 9.08 M. Compared to existing mainstream methods, the proposed method is lightweight, has low operating configuration requirements, high detection speed, and high accuracy, making it a feasible technical method and important tool for the early detection and diagnosis of colorectal cancer.

Research and implementation of intelligent diagnostic system for temporomandibular joint disorder

Temporomandibular joint disorder (TMD) is a common oral and maxillofacial disease, which is difficult to detect due to its subtle early symptoms. In this study, a TMD intelligent diagnostic system implemented on edge computing devices was proposed, which can achieve rapid detection of TMD in clinical diagnosis and facilitate its early-stage clinical intervention. The proposed system first automatically segments the important components of the temporomandibular joint, followed by quantitative measurement of the joint gap area, and finally predicts the existence of TMD according to the measurements. In terms of segmentation, this study employs semi-supervised learning to achieve the accurate segmentation of temporomandibular joint, with an average Dice coefficient (DC) of 0.846. A 3D region extraction algorithm for the temporomandibular joint gap area is also developed, based on which an automatic TMD diagnosis model is proposed, with an accuracy of 83.87%. In summary, the intelligent TMD diagnosis system developed in this paper can be deployed at edge computing devices within a local area network, which is able to achieve rapid detecting and intelligent diagnosis of TMD with privacy guarantee.

Artificial Intelligence-Assisted Daily Quality Control System for the Histologic Diagnosis of Gastrointestinal Endoscopic Biopsies: A 1-Year Experience.

Seegene Medical Foundation, one of the major clinical laboratories in South Korea, developed SeeDP, an artificial intelligence (AI)-based postanalytic daily quality control (QC) system that reassesses all gastrointestinal (GI) endoscopic biopsy (EB) slides for incorrect diagnoses. To review the operational records and clinical impact of SeeDP since its launch in March 2022. Operational records of SeeDP were retrieved for the period of March 1, 2022, to February 28, 2023. Among cases scanned during 40 working days (March 10, 2022, to May 4, 2022), all discordant cases encountered by 2 pathologists were reviewed. Cases of SeeDP-assisted revised diagnoses were collected and compared with cases recognized using conventional methods. Occasional scanner failures and various types of aberrant errors compromised QC coverage, resulting in the scanning of only 67.7% (572 254 of 844 906) of all EB slides submitted and 0.8% of the scanned slides being further excluded from the AI analysis. The AI predictions differed from the pathologists' diagnoses in 42 760 of the 557 672 gastrointestinal EB slides (7.7%) successfully assessed by the AI models; however, a detailed review of discordant slides revealed that true misdiagnosis accounted for only 5.5% (25 of 454) of the disagreements. Compared with conventional error recognition methods, SeeDP detected more misdiagnoses (7 versus 14) within a significantly shorter time (average, 3.6 versus 38.7 days; P < .001), including 1 signet ring cell carcinoma initially diagnosed as gastritis. AI-based daily QC systems are plausible solutions to guarantee high-quality pathologic diagnosis by enabling rapid detection and correction of misdiagnosis.

Miniaturized Electrochemical Gas Sensor with a Functional Nanocomposite and Thin Ionic Liquid Interface for Highly Sensitive and Rapid Detection of Hydrogen.

Hydrogen has been widely used in industrial and commercial applications as a carbon-free, efficient energy source. Due to the high flammability and explosion risk of hydrogen-air mixtures, it is vital to develop sensors featuring fast-responding and high sensitivity for hydrogen leakage detection. This paper presents a miniaturized electrochemical gas sensor by elaborately establishing a nanocomposite and thin ionic liquid interface for highly sensitive and rapid electrochemical detection of hydrogen, in which a remarkable response time and recovery time of approximately 6 s was achieved at room temperature. A screen-printed carbon electrode was modified with a reduced graphene oxide-carbon nanotube (rGO-CNT) hybrid and platinum-palladium (Pt-Pd) bimetallic nanoparticles to realize high sensitivity. To achieve miniaturization and high stability of the sensor, a thin-film room-temperature ionic liquid (RTIL) was employed as the electrolyte with a significantly decreased response time. The fast-responding hydrogen sensor demonstrates excellent performance with high sensitivity, linearity, and repeatability at concentrations below the lower explosive limit of 4 vol %. The engineered high-performance interface and gas sensor provide a promising and effective strategy for gas sensor design and rapid hazardous gas monitoring.

Falling weight impact acceleration-time signals analysis for road modulus detection: Theoretical and experimental investigations

Structural modulus detection is essential for construction and maintenance in road engineering. Falling weight impact loading method has significant strengths in large-scale, continuous, and rapid detection. To provide theoretical support for this, theoretical analysis and falling weight impact experiments were employed in this study to investigate the influence of road structure parameters and falling weight impact parameters on impact acceleration-time signals. First, the applicability of the Hertz model in road detection was analyzed, and theoretical formulas for peak acceleration amax and impact duration t2 were established, considering the perfectly elastic collision between falling weight and half-space. Second, the influence of layered structures’ properties on impact acceleration-time curves was studied through model box impact experiments. Third, field tests were conducted to study engineering applications. The results showed that the influence patterns were similar in both theoretical and experimental studies, with differences reflected in the time of compression and restitution phases due to the road materials’ viscosity and plasticity. Additionally, in indoor experiments, amax showed an excellent power function relationship with structural layer modulus, with R2 more than 0.95, and the contribution of each layer’s modulus to amax decreased from top to bottom. Finally, amax and resilient modulus of an old road also followed a good power function relationship, with R2 around 0.76. This study revealed that amax was recommended for road surface modulus detection based on solid theoretical and experimental support.

Development of graphene oxide-based biosensing platforms for label-free bioelectronic detection of pathogenic microorganisms.

A novel electrochemical nanogenosensor devoted to clinical analysis is reported for label-free detection of pathogenic microorganisms in the present work. The designed biosensor is composed of graphene oxide-modified disposable pencil graphite electrodes as a sensing platform. Escherichia coli is used as a model case. A 5'-aminohexyl-linked 22-base sequence probe representing the E. coli amplicon is immobilized onto sensor surfaces via carbodiimide chemistry. Hybridization is performed with denatured PCR amplicons of the bacteria. Detection is realized by transduction with the electrochemical impedance spectrometry technique. The selectivity of the designed genosensor is measured using Mycobacterium tuberculosis and Klebsiella pneumoniae sequences. Outstanding sensitivity is achieved with this genosensor array platform with a detection limit of 102 nM. This platform offers promise for rapid, simple, and cost-effective detection of various pathogenic microorganisms.

Highly sensitive multiplexed colorimetric lateral flow immunoassay by plasmon-controlled metal-silica isoform nanocomposites: PINs.

Lateral flow assay (LFA) systems use metal nanoparticles for rapid and convenient target detection and are extensively studied for the diagnostics of various diseases. Gold nanoparticles (AuNPs) are often used as probes in LFAs, displaying a single red color. However, there is a high demand for colorimetric LFAs to detect multiple biomarkers, requiring the use of multicolored NPs. Here, we present a highly sensitive multiplexed colorimetric lateral flow immunoassay by multicolored Plasmon-controlled metal-silica Isoform Nanocomposites (PINs). We utilized the localized surface plasmon resonance effect to create multi-colored PINs by precisely adjusting the distance between the NPs on the surface of PINs through the controlled addition of reduced gold and silver precursors. Through simulations, we also confirmed that the distance between nanoparticles on the surface of PINs significantly affects the color and colorimetric signal intensity of the PINs. We achieved multicolored PINs that exhibit stronger colorimetric signals, offering a new solution for LFA detection with high sensitivity and a 33 times reduced limit of detection (LOD) while maintaining consistent size deviations within 5%. We expect that our PINs-based colorimetric LFA will facilitate the sensitive and simultaneous detection of multiple biomarkers in point-of-care testing.

Nanobody-Induced Aggregation of Gold Nanoparticles: A Mix-and-Read Strategy for the Rapid Detection of Cronobacter sakazakii.

Protein-nanoparticle interactions play a crucial role in both biomedical applications and the biosafety assessment of nanomaterials. Here, we found that nanobodies can induce citrate-capped gold nanoparticles (AuNPs) to aggregate into large clusters. Subsequently, we explored the mechanism behind this aggregation and proposed the "gold nucleation mechanism" to explain this phenomenon. Building on this observation, we developed a one-step label-free colorimetric method based on nanobody-induced AuNP aggregation. When nanobodies bind to target bacteria, spatial hindrance occurs, preventing further AuNPs aggregation. This alteration in surface plasmon resonance properties results in visible color changes. As an example, we present a simple and sensitive "mix-and-read" chromogenic immunosensor for Cronobacter sakazakii (C. sakazakii). The experiment can be completed within 20 min, with a visual detection limit of 103 CFU/mL and a quantitative detection limit of 136 CFU/mL. Importantly, our method exhibits no cross-reactivity with other bacterial species. This strategy harnesses the excellent properties of nanobodies and the optical characteristics of AuNPs for direct and rapid detection of foodborne pathogen.

PcMNet: An efficient lightweight apple detection algorithm in natural orchards

Apple detection plays a critical role in enabling the functionality of harvesting robots within natural orchard environments. To address challenges related to low detection accuracy, slow inference speed, and high parameter count, we present PcMNet, a lightweight detection model based on an improved YOLOv8 network. Initially, we employed Partial Convolution (Pconv) to construct a PR module, forming the Pconv-block, which subsequently replaced the original C2f feature extraction module within the YOLOv8n backbone. This replacement led to improvements in both detection accuracy and speed, while simultaneously reducing computational complexity (FLOPs), parameter count, and model size. Furthermore, the Cross-Scale Feature Fusion (CCFF) module was refined into Faster-Cross-Scale Feature Fusion (Faster-CCFF) with the integration of Pconv-block, significantly enhancing the model's feature extraction and fusion capabilities. Additionally, we introduced Mixed Local Channel Attention (MLCA) to further strengthen the model's capacity to capture essential features while effectively suppressing background noise. Experimental results demonstrate that PcMNet achieved a detection accuracy of 92.8 % and an mAP@0.5 of 95.5 %, representing improvements of 1.4 and 0.7 percentage points, respectively, over YOLOv8n. Moreover, PcMNet successfully reduced FLOPs, parameter count, and model size to 5.1 G, 1.4 M, and 3.2 MB, respectively. The per-image detection time was reduced to 2.3 ms, indicating reductions of 37.80 %, 53.33 %, 49.21 %, and 56.60 % in FLOPs, parameters, model size, and detection time compared to YOLOv8n. When deployed on edge computing devices with TensorRT acceleration, PcMNet achieved a detection rate of 92 FPS. Field validation experiments conducted in natural orchard environments confirmed PcMNet's superior ability to detect apples under challenging conditions, such as occlusions and varying lighting conditions. Its lightweight design and rapid detection capabilities provide a valuable reference for achieving real-time apple detection in automated and intelligent harvesting robots, thereby contributing to advancements in smart agriculture.